Fuzzy Logic in Environmental Sciences: A Bibliography

Here is a large collection of references to environmental research and meteorologial applications that use fuzzy logic. Researchers are listed alphabetically:
A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z
Domains of interest include:
Agriculture, climatology, earthquakes, ecology, environmental sciences, fisheries, geography, geology, hydrology, meteorology, mining, natural resources, oceanography, petroleum, pollution, risk analysis, and rivers+lakes.
If you know of work that fits in these categories, please write to us and we will add them. If you would like to be included in the list below, send us a note about yourself and your topics.

We are preparing a chapter on fuzzy logic for a handbook in AI. The handbook is being assembled by AIRIES (Artificial Intelligence Research in Environmental Sciences). The book is intended to introduce environmental scientists to practical, state-of-the-art AI techniques. Many references were kindly given to us when we posted questions on newsgroups for the above-listed domains and on the met.ai newsgroup and fuzzy logic newsgroup.

The AIRIES Fuzzy Team includes Rich Bankert, Mike Hadjimichael, and Bjarne Hansen.

We do not regard fuzzy logic as a technique by itself. Rather, we regard it as a method that can enhance other techniques. For instance, artificial neural networks can be trained faster when parameters are expressed in fuzzy terms. The diversity of the applications described below shows how fuzzy logic is becoming a mainstream technique in many different fields.

P.S. Most of the references listed here were collected in 1997. The rest have been added since then thanks to readers' contributions. The last one added was on 14 July 2004.

New listings are flagged with   New



Jon Ahlquist
email: ahlquist@met.fsu.edu

I had two papers published in "Fuzzy Sets and Systems" (1987, vol. 22, 229-244 and 1988, vol. 25, 87-95) which looked at the possibility of using fuzzy implication to probe nonsymmetric relationships (i.e., A -> B being different from B -> A) in data. The purpose of this was to look for nonlinear relationships, since, linearly, A is correlated to B to the same degree as B is correlated to A. I was less than thrilled with the outcome since contradictory results can be had if different fuzzy implication operators are used. Consequently, I have done nothing more with fuzzy logic since then. See the published articles for particulars.

Martin Ameskamp
Research assistant at the Computer Science Department
of the Christian-Albrechts-Universität Kiel, Germany.
email: ma@informatik.uni-kiel.de

Research interests:

I recently completed a PhD thesis entitled: Three-dimensional Rule-based Continuous Soil Modelling.. This thesis is available as Technical Report 9701 of the Kiel Computing Department, and it is available as a variety of postscript files here. A complete list of my publications (some of them available as postscript files) is here.

Antony S.
Flexible Intelligence Group, LLC
Tuscaloosa, AL, USA
email: info@flextool.com

We are in the process of applying fuzzy logic to agriculture and fisheries.

Bachisio Arca, F. Benincasa, Matteo De Vincenzi
Institute for Agroecosystem Monitoring
Sassari, Italy
email: arca@hpj.area.ss.cnr.it, matteo@hpj.area.ss.cnr.it

We have applied fuzzy logic and neural network technique to modelling complex physical and biological processes (solar radiation fluxes, evaporation, evapotranspiration, ground frost). In particular two neural networks were developed for estimating net and diffuse radiation using some meteorological parameters (global radiation, reflected radiation, air temperature, air relative humidity, soil temperature, sunshine); these neural networks enhanced the precision with respect to empirical model and provide useful information for the analytical formulation of phenomena.

Recent work:

Neural nets to estimate radiative fluxes (net solar radiation, diffuse radiation)
Thermodynamic compensation of ultrasonic range finder using a neural network
Estimate Pan evaporation (A class) using a neural network
Planning neuronal model to predict night minimum temperature

Rich Bankert
Naval Research Laboratory
Monterey, California
email: bankert@nrlmry.navy.mil

Research Interests:
cloud classification
artificial intelligence in environmental science
MEDEX: fuzzy rule-based system for predicting the onset and cessation of gale force winds.

Dr. Andras Bardossy
University of Stuttgart, Germany
email: bardossy@iws.uni-stuttgart.de

Has written numerous papers and at least one book on this subject, especially related to meteorology and hydrology.

Brad Bass
Environmental Adaptation Research Group, Institute for Environmental Studies
email: bassb@aestor.am.doe.ca

Research Interests: Complexity and self-organization particularly in relation to adaptation to environmental change.

Dr. Istvan Bogardi
University of Nebraska
email: ibogardi@unlinfo.unl.edu

Current Research:
  1. Prediction of local hydrology under global climate change (National Science Foundation)
  2. Impact of climate change on water quality (NATO)
  3. Effect of global climate change on regional hydrology (National Science Foundation)
  4. Incorporation of fuzzy set techniques to account for uncertainties in extremes under changing climatology (National Science Foundation)
  5. Working together: integration engineering, the humanities and the social sciences to prevent environmental degradation (U.S. Information Agency)
  6. Space-time local hydrology influenced by changing climatology: disaggregation, prediction and comparison (National Institute for Global Environmental Change, Great Plains Regional Center)

Dr. Trevor H. Booth
CSIRO Division of Forestry and Forest Products
Canberra, Australia
email: Trevor.Booth@acacia.cbr.for.csiro.au

We use a program called Plantgro. It uses "notional relationships" which are simple two dimensional graphs with linear segments which describe the suitability of individual soil and climate factors in terms of 0-9 ratings. The overall effect on plant growth is then taken as the minimum of all the factors. It doesn't claim to be "fuzzy logic", but in terms of my naive knowledge of fuzzy logic it seems pretty close. I'd be happy to mail you some information if it's of interest.

Prof.Dr.ir. Dick Botteldooren, ir. Tom De Muer, Andy Verkeyn
Acoustics Research Group
Department of Information Technology
Ghent University, Belgium
email: {dick.botteldooren|tom.demuer|andy.verkeyn}@rug.ac.be

We are applying soft computing techniques to the modeling of environmental noise in its broadest sense. Data from the the driving forces (such as road traffic, rail traffic, industry,...), are used to quantify the sound emission. However, as environmental data is often expensive to collect, it is scarce, vague and uncertain (e.g. amount of traffic can only be estimated). To fully exploit this kind of data, fuzzy sound propagation models are being built for the assessment of the state of the (noise) environment. Noise annoyance is generally considered a good indicator for the adverse health effects (impact) of noise pollution on man, such as sleep disturbance, stress, high bloodpressure,... Annoyance, which is in itself a fuzzy concept, not only depends on the sound exposure, also a lot of personal and psychological factors have their influence. Those relations are typically very subjective and still uncertain to a large degree, hence, fuzzy rule based models are appropriate for the modeling of annoyance.

For more information and a list of our publications, please consult our website: http://www.intec.rug.ac.be/groupsites/acoustics

Belinda Choi
email: b.choi@latrobe.edu.au

Interprets satellite radiance data.

Tom Clair
Environmental Conservation Branch
Sackville, N.B., Canada
email: tom.clair@ec.gc.ca

J.M. Ehrman, T.A. Clair, and A. Bouchard "Using neural networks to predict pH changes in acidified eastern Canadian lakes. " AI Applications, Vol. 10, No. 2, 1996

Abstract: Predicting changes in the pH of lakes with changes in acid precipitation is a difficult endeavor, especially in areas with high concentrations of natural dissolved acids, such as are found in much of eastern Canada. Statistical techniques have been used in a number of studies to try to predict changes in large areas, but cannot be used with confidence because of autocorrelation between variables. Because of the difficulties encountered with chemical and statistical techniques, we used a neural net approach to see if patterns could be detected in water chemistry data. We produced a model that was able to accurately predict the current pH of 164 lakes within 2% of measured values. We then varied lake sulphate concentration, the main acidification input to this region, to see how the lake pH would change. The neural net approach seemed more sensitive than statistical approaches in making predictions.


References to fuzzy logic in biology

  1. Ameskamp, M. and J. Lamp.  1996.  Three dimensional Soil Prediction:  Fuzzy Rules and a GIS.  Presented at Land-Information Systems: Developments for planning the sustainable use of land resources, Hanover 20-23 Nov. 1996  Proceedings to be published by European Commission.
  2. Ameskamp, M..  1996.  Three-Dimensional Rule-Based Continuous Soil Modeling.  PhD thesis, Faculty of Engineering, Kiel University.  Technical Report 9701, Institute of Computer Science and Applied Mathematics, Kiel.
  3. Bardossy, A. and M. Disse.  1993.  Fuzzy rule-based models for infiltration.  Water Resources Research. Feb Vol. 29 (2) pp. 373-382.
  4. Bardossy, A., I. Bogardi and W.E. Kelly.  1989.  Geostatistics Utilizing Imprecise (Fuzzy) Information.  Fuzzy Sets and Systems 31 pp. 311-328.
  5. Bardossy. A. and L. Duckstein.  1992.  Analysis of a Karstic Aquifer Management Problem by Fuzzy Composite Programming.  Water Resources Bulletin Vol 28(1) Feb.   pp. 63-73.
  6. Bare, B. and Mendoza, G.  1988. "A soft optimization approach to forest land management planning." Canadian Journal of Forest Research. Vol.18, p.545-552.
  7. Bare, B. and Mendoza, G.  1990. "Fuzzy programming approaches in handling uncertainty and imprecision in timber harvest scheduling." A paper presented at the Management Science/Operations Research Working Group Session at the SAF National Convention, Washington, D.C.
  8. Bare, B. and Mendoza, G.  1992. "Timber harvest scheduling in a fuzzy decision environment." Canadian Journal of Forest Research. Vol.22, p.423-428.
  9. Bezdek, J. Trivedi, M., Ehrilich, R. and Full, W. (1981). "Fuzzy clustering: a new approach for geostatistical analysis." Int. J. of Systems, Measurement, and Decisions, 2, p.13-23.
  10. Bezdek, J.C. and K. Solomon.  1981.  Simulation of Implicit Numerical Characteristics Using Small Samples.  Applied Systems and Cybernetics. Vol. VI: Fuzzy Sets and Systems, Possibility Theory and Special Topics in System Research. 1981. pp. 2773-2784.
  11. Bogardi, I., A. Bardossy and L. Duckstein.  1990.   Risk Management for Groundwater Contamination:  Fuzzy Set Approach.  In: Optimizing Resources for Water Management.  Proceedings of the ASCE 17th Annual National Conference Apr. 17-21 Fort Worth, TX.  pp. 445-449.
  12. Bosserman, R. and Ragade, R.  1982. "Ecosystem analysis using fuzzy set theory." Ecological Modeling, 16, p.191-208.
  13. Burrough, P.A.  1989. "Fuzzy mathematical methods for soil survey and land evaluation." J. of Soil Sci., 40, p.477-492.
  14. Burrough, P.A.  1989.  Fuzzy mathematical methods for soil survey and land evaluation.  Journal of Soil Science, Vol. 40.  pp. 477-492.
  15. Chameau. J. and Santamarina, J.  1989. "Knowledge-based system for soil improvement." J. of Computing in Civil Engineering, Vol.3, No.3, July, p.253-265.
  16. Chang, L and Burrough, P.  1987.  "Fuzzy reasoning: a new quantitative aid for land evaluation." Soil Survey and Land Evaluation, 7, p.69-80.
  17. Clyma, H.E. and D.L. Martin.  1996.  Forecasting Evapotranspiration using Fuzzy Logic.  Paper No. 962100.  ASAE, 2950 Niles Rd., St. Joseph, MI 49085-9659.
  18. Clyma, H.E. and D.L. Martin.  1996.  Irrigation management using fuzzy logic.  In: Evapotranspiration and irrigation scheduling.  Ed. Camp,C.R., E.J. Sadler, and R.E. Yoder.  Proceedings of the International Conference, San Antonio, Texas, USA, November 3-6.  pp. 1134-1139.
  19. Deshpande, A.W. , D.V. Raje and P.Khanna.  1996.  Agreement Index for Water Consumption. Eufit '96, September 2-5, 1996. pp. 1816-1820.
  20. Deshpande, A.W., D.V. Raje and P. Khanna.  1996.  Fuzzy Description of River Water Quality.  Eufit '96, September 2-5.  pp 1795-1801.
  21. Dou, C., W. Woldt, I. Bogardi, and M. Dahab.  1996.  Modeling Groundwater Flow and Transport within a Fuzzy Set Framework:  Part II - Solute Transport.  Eufit, '96, September 2-5.  pp 50-54.
  22. English, M.J.  1981.  The uncertainty of crop models in irrigation optimization.  Trans. of the ASAE.  Nov/Dec 34(6):917-.
  23. Ferson, S. and T.F. Long. 1998.  Deconvolution Can Reduce Uncertainty In Risk Analyses.  In: Risk Assessment:  Logic and Measurement. ed. M.C Newman and C.L Strojans.  United States Sleeping Bear Pr-Ann Arbor.
  24. Flick, R. and van Kooten, G.  1993. "Modelling management of agricultural ecosystems using fuzzy set theory: methodological issues." Paper presented at the joint meetings of the Western Agricultural Economics Association and the Canadian Agricultural Economics and Farm Management Society, 1993, Edmonton, Alberta.
  25. Fox, F.A. and D.C. Slack.  1997.  Irrigation Scheduling Decision Support.  Computers in Agriculture 1994  Proceedings of the 5th International Conference 6-9 Feb. 1997 Orlando FL.
  26. Gowing, J., A. Tarimo and O. El-Awad.  1996.  A rational method for assessing irrigation performance at farm level with the aid of fuzzy set theory.  Irrigation and Drainage Systems 10:319-330.
  27. King, J.P., I. Broner, R.L. Croissant, and C.W. Basham.  1991.  Malting barley water and nutrient management knowledge-based system.  Trans. of the ASAE.  Nov/Dec Vol. 34(6):2622-2630.
  28. Kollias, V.J. and A. Voliotis.  1991.  Fuzzy reasoning in the development of geographical information systems.  International Journal of Geographical Information Systems.  Vol 5(2). pp. 209-223.
  29. Kollias, V.J., and Voliotis, A.  1991.  "Fuzzy reasoning in the development of geographical information systems. FRSIS: a prototype soil information system with fuzzy retrieval capabilities." Int. J. Geographical Information Systems 5, p.209-223.
  30. Kurata, K. and N. Eguchi.  1990.  Machine learning of fuzzy rules for crop management in protected cultivation.  Trans. of the ASAE. Jul/Aug Vol. 33(4):1360-1368.
  31. Lacroix, R., M. Strasser, K. Wade and R. Kok.  1994.  A Fuzzy-logic Based Decision-Support System for Dairy Cattle Breeding.  Presented at the 1994 International Winter ASAE Meeting December 13-16, 1994 in Atlanta, GA.
  32. Lee, Y.W., M.F. Dahab and I. Bogardi.  1992.  Nitrate Risk Management Under Uncertainty.  Journal of Water Resources Planning and Management. Vol. 118, No. 2, March/April.  pp 151-164.
  33. Leung, Y..  1982.  Approximate Characteristics of Some Fundamental Concelpts of Spatial Analysis.  Geographical Analysis, Vol. 14, No. 1, January.  pp 29-40.
  34. Malano, H.M. and G. Gao.  1992.  Ranking and Classification of Irrigation system performance using fuzzy set theory: case studies in Australia and China.  Irrigation and Drainage Systems 6: 129-148.
  35. Mays, M.D., I. Bogardi and A. Bardossy.  1995.  Managing Risk and Variability with Fuzzy Soil Interpretations.  In:  Site-Specific Management for Agricultural Systems.  ASA-CSSA-SSSA, Madison, WI. pp 187-198.
  36. McBratney, A.B. and B.M. Whelan.  1995.  Continuous Models of Soil Variation For Continuous Soil Management.  In:  Site Specific Management for Agricultural Systems.  pp. 325-338.
  37. McBratney, A.B. and Moore, A.W.  1985.  "Application of fuzzy sets to climatic classification." Agricultural and Forest Meteorology, 35, p.165-185.
  38. McBratney, A.B., De Gruijter, J.J., Brus, D.J.  1992.  "Spacial prediction and mapping of continuous soil classes." Geoderma, 54, p.39-64.
  39. McBratney, A.B., J.J. De Gruijter and D.J. Brus.  1992.  Spacial prediction and mapping of continuous soil classes.  Geoderma, Vol. 54.  pp. 39-64.
  40. Mendoza, G. and Sprouse, W.  1989.  "Forest planning and decision-making under fuzzy environments: an overview and illustrations." Forest Science, Vol. 35, No.2, p.481-502.
  41. Nielsen, D.R., O. Wendroth and M.B. Parlange.  1995.  Opportunities for Examining On-Farm Soil Variability.  In:  Site-Specific Management for Agricultural Systems. ASA-CSSA-SSSA, Madison, WI. pp 95-132.
  42. Odeh, I., McBratney, A. and Chittleborough, D.  1990.  "Design of optimal sample spacings for mapping soil using fuzzy-k-means and regionalized variable theory." Geoderma, 47, p.93-122.
  43. Odeh, I., McBratney, A. and Chittleborough, D.  1992.  "Fuzzy-c-means and kriging for mapping soil as a continous system." Soil Sci. Soc. Am. J., Vol.56, Nov-Dec., p.1848-1854.
  44. Odeh, I.O.A., A.B. McBratney and D.J. Chittenbrough.  1992.  Fuzzy-c-Means and Kriging for Mapping Soil as a Continuous System.  Soil Science American Journal. Vol 56.  pp. 1848-1854.
  45. Odeh, I.O.A., A.B. McBratney and D.J. Chittleborough.  1990.  Design of optimal sample spacings for mapping soil using fuzzy-k-means and regionalized variable theory.  Geoderma. Vol 47.  pp. 93-122.
  46. Pickens, J. and Hof, J.  1991.  "Fuzzy goal programming in foresty: an application with special solution problems." Fuzzy Sets and Systems, 39, p.239-246.
  47. Pickens, J., Hof, J. and Bartlett, E.  1987.  "Fuzzy goals, MAXIMIN programming, and natural resource management." The 1985 Symposium on Systems Analysis in Forest Resources, Athens, Georgia.
  48. Powell, B., A.B. McBratney and D.A. Macleod.  1992.  Fuzzy classification of soil profiles and horizons from the Lockyer Valley, Queensland, Australia.  Geoderma.  Vol 52. pp. 173-197.
  49. Powell, B., McBratney, A., and MacLeod, D.  1991.  "The application of ordination and fuzzy classification techniques to field pedology and soil stratigraphy in the Lockyer Valley, Queensland."  Catena, 18, p.409-420.
  50. Powell, B., McBratney, A., and MacLeod, D.  1992.  "Fuzzy classification of soil profiles and horizons from the Lockyer Valley, Queensland, Australia." Geoderma, 52, p.173-197.
  51. Ribeiro, S.F. and R.E. Yoder.  1997.  An Automated Fuzzy Irrigation Control System.  In:  Proceedings Irrigation Association Technical Conference.  Nov 2-4, 1997, Nashville TN:  pp. 171-178.
  52. Roberts, D.  1989.  "Analysis of forest succession with fuzzy graph theory." Ecological Modelling, 45, p.261-274.
  53. Roush, W.  1989. "Fuzzy decision analysis." Egg Industry, May.
  54. Roush, W., Bock, R., and Marszalek, M.  1989. "Evaluation of crowding of caged laying hens using fuzzy decision analysis." Appl. Anim. Behav.Sci..
  55. Savchenko, C. and A. Borisov.  1997.  Decision Making Based on a Bayesian Belief Network.  Eufit '97, September 8-11.  pp. 72-76.
  56. Schmoldt, D.L.  1989. "An influence model for qualitative simulation of ecological systems." Artificial Intelligence and Growth Models for Forest Management Decisions, p.339-344.
  57. Schmoldt, D.L.  1990. "A simulation of plant physiological processes using fuzzy variables." Proceedings of the 1990 National Conference on Artificial Intelligence.
  58. Sher, A. and I. Amir.  1994.  Optimization with Fuzzy Constraints in Agricultural Production Planning.  Agricultural Systems. Vol. 45.  pp 421-441.
  59. Sher, Arnold and Amir, Ilan  1994. "Optimization with fuzzy constraints in agricultural production planning." Agricultural Systems, 45, p.421-441.
  60. Tang, H.J. and Van Ranst, E.  1992. "Testing of fuzzy set theory in land suitability assessment for rainfed grain maize production." Pedologie, XLII-2, p.129-147.
  61. Tang, H.J., Debaveye, J., Ruan, D., and Van Ranst, E.  1991. "Land suitability classification based on fuzzy set theory." Pedologie, XLI-3, p.277-290.
  62. Thangavadivelu, S. and T.S. Colvin.  1991.  Problem Solving in Farm Management Using Fuzzy Set Theory.  Presented by S. Thangavadivelu and T.S. Colvin at the 1991 Mid-Central ASAE Meeting April 19-20, 1991 at St. Joseph, MO.
  63. Wenger, R. and Yue Rong  1987. "Two fuzzy set models for comprehensive environmental decision-making." J. of Env. Mgt., 25, p.167-180.
  64. Woldt, W., C. Dou, I. Bogardi and M. Dahab.  1996.  Modeling Groundwater Flow and Transport within a Fuzzy Set Framework:  Part I - Flow.  Eufit, '96, September 2-5.  pp 159-163.
  65. Xiang, H., B.P. Verma, and G. Hoogenboom.  1994.  Fuzzy irrigation decision support system.  Paper No. 943557.  ASAE, 2950 Niles Rd., St. Joseph, MI 49085-9659.
  66. Yee, L.  1987. "On the imprecision of boundaries." Geographical Analysis, 19, p.125-151.

Dr. Steve Dorling
Lecturer in Atmospheric Sciences
University of East Anglia, UK
email: s.dorling@uea.ac.uk

At an AIRIES conference, Dr. Dorling presented a system to automatically evaluate the Lamb Index. The Lamb Index indicates the type of synoptic situation influencing the UK. It is based on the orientation of high and low pressure centres and on the cyclonic/anticyclonic nature of the wind field. Determination of the Lamb Index has traditionally been a subjective procedure. The developmental system replaces subjectivity with fuzzy logic. The system will be used in a reanalysis of weather maps dating back to about 1860.

Other interests:

Applying neural networks to climate change.
'Weather Typing' using neural nets.
Fuel burning and carbon dioxide release.
Meteorological influences on particulate transport across Europe.
Climate change and air pollution in Europe.
Circulation patterns and source-receptor relationships

Connie Dou

Here is one of my published papers that use fuzzy logic in environmental engineering related areas:

"Steady state groundwater flow simulation with imprecise parameters" by C. Dou et al., published in Water Resources Research, vol. 31(11), pp. 2709-2719, 1995.

If you need a copy of the above paper, please let me know. I also have two other papers that are in publishing process.

BTW, do you know anybody who is looking for a post-doctor position or other job opening that related to application of fuzzy logic in environmental related field, or hydrology or modeling? I am looking for a job right now. Could you please let me know if you have any information on that?

Norbert Driedger
Meteorologocal Service of Canada
Toronto, Canada
email: norbert.driedger@ec.gc.ca


Norbert Driedger and Brian Greaves, 2001: Towards a Fuzzy Spatial Database for Nowcasting, Proceedings of SVUG01: The Joint COSIT-FOIS Workshop on Spatial Vagueness, Uncertainty and Granularity.

Oscar Duarte
Departamento de Ingeniería Eléctrica (Department of Electrical Engineering)
Universidad Nacional de Colombia (National University of Colombia)

We have developed a fuzzy methodology for Environmental Impact Analysis and we have also designed a software tool in order to apply such methodology. A brief presentation of the main ideas underlying the methodology are avalaible at: www.ing.unal.edu.co/~ogduarte (select "publicaciones")

Duarte O., Delgado M., Requena I. "Application of the extension of crisp functions to fuzzy numbers in the Environmental Impact Analysis" Proceedings of the Eighth International Conference IPMU 2000 (Information Processing and Managment of Uncertainity in Knowledge-based Systems) Madrid 2000.

The whole presentation of the methodology is also available (in Spanish) in the same web site:

Duarte O. "Técnicas Difusas en la Evaluación de Impacto Ambiental" Ph.D. Thesis, Universidad de Granada. 2000

You can download the software tool (with an Spanish interface) in: www.ing.unal.edu.co/~ogduarte (select "software")

TDEIA: Técnicas Difusas en la Evaluación de Impacto Ambiental

Our methodology is, in few words, a fuzzy version of the crisp cause-effect matrix methods (i.e. Leopold matrix). We can combine numeric and lingüistic evaluation of partial impacts. More important, we can give a clear definition of all subjective variables involved in the environmental impact analysis.We can also suggest the type of correction actions to be implemented.

To do that, we developed a kind of "Computing with words" system, based on fuzzy arithmetic, rather than fuzzy logic. This kind of system is also explained in the previous references.

Peter Dzikowski, P.Ag.
Weather Resource Specialist
Alberta Agriculture, Food and Rural Development
Edmonton, Alberta, Canada
email: peter.dzikowski@agric.gov.ab.ca

I suggest you browse through the proceedings of the "Computers in Agriculture" conference series that Fedro Zazueta, U of Florida has put together. There are quite a few presentations describing fuzzy logic in decision support or in process control systems.

Scott Ferson
email: scott@ramas.com

Senior Scientist at Ramas Software. Ramas develops software for ecology, conservation biology, wildlife management, and public health risk analysis. RAMAS software is used by over 1000 institutions and thousands of students in 40 countries, both for teaching and in applied research. Ferson's interests in fuzzy methods for environmental sciences focus on the use of fuzzy arithmetic (sensu Kaufmann and Gupta) in ecological risk analysis. For the last three years, he has organized workshops on the topic under the sponsorship of the Society for Risk Analysis.

Papers on the subject are listed below. Most of them appeared in gray literature, so feel free to request (p)reprints by email. References [2, 3, 4, 10] describe or cite software packages that implement fuzzy arithmetic for use in risk analysis. References [5, 11] describe applications to specific fisheries management problems. References [6, 7, 8] describe "hybrid" arithmetic that combines probability theory and fuzzy arithmetic. Reference [12] describes comparisons between fuzzy arithmetic and various probabilistic approaches to risk analysis.

  1. Ferson, S. 1989. Review of "Possibility Theory: An Approach to Computerized Processing of Uncertainty" by Didier Dubois and Henri Prade. The Quarterly Review of Biology 64:241.
  2. Ferson, S. and R. Kuhn. 1992. Propagating uncertainty in ecological risk analysis using interval and fuzzy arithmetic. Computer Techniques in Environmental Studies IV, P. Zannetti (ed.), Elsevier Applied Science, London, pp. 387-401.
  3. Ferson, S. and R. Kuhn 1994. Interactive microcomputer software for fuzzy arithmetic. Proceedings of the High Consequence Operations Safety Symposium, Sandia National Laboratories, Report SAND94-2364, J.A. Cooper (ed.), pp. 493-506.
  4. Kuhn, R. and S. Ferson. 1994. Risk Calc. Applied Biomathematics, Setauket, New York. [Commercially distributed software for Windows 3.1, including a 49-page manual].
  5. Ferson, S. 1994. Using fuzzy arithmetic in Monte Carlo simulation of fishery populations. Management Strategies for Exploited Fish Populations, G. Kruse et al. (eds.), proceedings of the International Symposium on Management Strategies for Exploited Fish Populations, Anchorage, 1992, Alaska Sea Grant College Program, AK-SG-93-02, pp. 595-608.
  6. Ferson, S. and L. Ginzburg. 1995. Hybrid arithmetic. Proceedings of the 1995 Joint ISUMA Third International Symposium on Uncertainty Modeling and Analysis and NAFIPS Annual Conference of the North American Fuzzy Information Processing Society, IEEE Computer Society Press, Los Alamitos, California, pp. 619-623.
  7. Cooper, J.A., S. Ferson and L.R. Ginzburg. 1995. Hybrid processing of stochastic and subjective uncertainty datab. Sandia National Laboratories, Report SAND95-2450, Albuquerque, New Mexico.
  8. Cooper, J.A., S. Ferson and L.R. Ginzburg. 1996. Hybrid processing of stochastic and subjective uncertainty data. Risk Analysis 16: [in press].
  9. Ginzburg, L.R., C. Janson, and S. Ferson. 1996. Judgment under uncertainty: evolution may not favor a probabilistic calculus. Behavioral and Brain Sciences 19: 24f.
  10. Ferson, S. 1996. Fuzzy Arithmetic Demo. Applied Biomathematics, Setauket, New York. [DOS software]
  11. Saila, S. and S. Ferson. 1996. Uncertainty analysis using simple fishery models. [presented at international fisheries conference, Azores, June, 1996].
  12. Moore, D. and S. Ferson. Probabilistic exposure analysis: possible approaches when information is limited (as it always is). [in preparation].

Robert Flick
Agricultural Economics, UBC
Vancouver, B.C.
email: flick@unixg.ubc.ca

Due to the number of requests I've received requesting information on fuzzy logic for agricultural and forestry modelling, I am forwarding below a list of the articles that I've found to date. With respect to fuzzy set theory itself, perhaps the best introduction to the subject is H.-J. Zimmermann's book: Fuzzy Set Theory and its Applications, 2nd, revised edition, published by Kluwer Academic Publishers, 1991.

  1. Tang Huajun & E. Van Ranst, "Testing of Fuzzy Set Theory in Land Suitability Assessment for Rainfed Grain Maize Production", Pedologie, XLII-2, p129-147, 1992;
  2. Tang Huajun et.al., "Land Suitability Classification Based on Fuzzy Set Theory", Pedologie, XLI-3, p277-290, 1991;
  3. Guillermo Mendoza & William Sprouse, "Forest Planning and Decision-Making under Fuzzy Environments: An Overview and Illustrations", Forest Science, Vol. 35, No.2, p481-502, 1989;
  4. David Roberts, "Analysis of Forest Succession with Fuzzy Graph Theory", Ecological Modelling, 45, p261-274, 1989;
  5. Robert Bosserman & Rammohan Ragade, "Ecosystem Analysis using Fuzzy Set Theory", Ecological Modelling, 16, p191-208, 1982;
  6. Inakwu Odeh et.al., "Fuzzy-c-Means and Kriging for Mapping Soil as a Continuous System", Soil Sci. Soc. Am. J., Vol.56, Nov-Dec., 1992;
  7. P.A.Burrough, "Fuzzy Mathematical Methods for Soil Survey and Land Evaluation", J. of Soil Sci., 40, p477-492, 1989;
  8. D.L.Schmoldt, "An Influence Model for Qualitative Simulation of Ecological Systems", Artificial Intelligence and Growth Models for Forest Management Decisions", 1989;
  9. James Pickens, John Hof, and E.T.Bartlett, "Fuzzy Goals, MAXIMIN Programming, and Natural Resource Management", (? Proc. of the Int'l Symposium on Systems Analysis and Management Decisions in Forestry, Valdivia, Chile?);
  10. Bruce Bare and Guillermo Mendoza, "Fuzzy Programming Approaches in Handling Uncertainty and Imprecision in Timber Harvest Scheduling", (in above?)
  11. Bruce Bare and Guillermo Mendoza, "Timber Harvest Scheduling in a Fuzzy Decision Environment", Can.J.For.Res., Vol.22, 1992;
  12. Bruce Bare and G.Mendoza, "A Soft Optimization Approach to Forest Land Management Planning", Can.J.For.Res., Vol.18, 1988;
  13. A.B. McBratney and A.W. Moore, "Application of Fuzzy Sets to Climatic Classification", Agricultural and Forest Meteorology, 35, p165-185,1985;
  14. William Roush, "Fuzzy Decision Analysis", Egg Industry, May, 1989;
  15. Robert Flick, "Modelling Management of Agricultural Ecosystems using Fuzzy Set Theory: Methodological Issues", paper presented at the joint meetings of the Western Agricultural Economics Association and the Canadian Agricultural Economics and Farm Management Society, 1993, Edmonton, Alberta.
this set of references is copied with permission from http://www.spatial.ca/~tmoore/fmdss/1994/msg00080.html

Mark R. Fulton
Dept. of Ecology and Evolutionary Biology
Rice University
Houston, TX 77005
email: markf@lucybraun.rice.edu
Fulton, M.R. 1996. An application of fuzzy set ordination to determine tree habitat suitability of sites from a regional data set. Journal of Vegetation Science 7: 739-746.

Terese Gabocy
email: tag6204@unm.edu

There are several references in "Fuzzy Systems and Sets" for environmental uses. Also, Dr. Duckstein, I. Bogardi, and A. Bardossy are good authors to look up.

My thesis involves the use of fuzzy math and logic for ecological risk assessment.


There are a few papers in the journal _Vegetatio_ on the subject of application of fuzzy logic to ecological systems. For instance: David W. Roberts (then at the Department of Forest Resources and Ecology Center at Utah State University), "Fuzzy Systems Vegetation Theory," _Vegetatio_, volume 83, pp 71-80, dated 1989

Mike Hadjimichael
Naval Research Laboratory
Monterey, California
email: hadjimic@nrlmry.navy.mil

Research interests

  • AI in meteorology
  • Knowledge Discovery from Databases
  • Soft Computing
  • Rough Set Theory

  • Bjarne Hansen
    Meteorological Research Branch
    Meteorological Service of Canada
    Montreal, Quebec, Canada
    email: bjarne@cs.dal.ca

    List of publications
    AI and meteorology


    The following 18 American Meteorological Society (AMS) journal articles describe meteorological applications of fuzzy logic. The articles were found in March 2003 by examining abstracts of articles found using the using the AMS search engine, ams.allenpress.com.

    1. Gautam, Nalin K., N. D. Kaushika, 2002: A Model for the Estimation of Global Solar Radiation Using Fuzzy Random Variables. Journal of Applied Meteorology: Vol. 41, No. 12, pp. 1267-1276.
    2. Bianco, Laura, James M. Wilczak, 2002: Convective Boundary Layer Depth: Improved Measurement by Doppler Radar Wind Profiler Using Fuzzy Logic Methods. Journal of Atmospheric and Oceanic Technology: Vol. 19, No. 11, pp. 1745-1758.
    3. Morse, Corinne S., Robert K. Goodrich, Larry B. Cornman, 2002: The NIMA Method for Improved Moment Estimation from Doppler Spectra. Journal of Atmospheric and Oceanic Technology: Vol. 19, No. 3, pp. 274-295.
    4. Cohn, Stephen A., Robert K. Goodrich, Corinne S. Morse, Eli Karplus, Steven W. Mueller, Larry B. Cornman, R. Andrew Weekley, 2001: Radial Velocity and Wind Measurement with NIMA-NWCA: Comparisons with Human Estimation and Aircraft Measurements. Journal of Applied Meteorology: Vol. 40, No. 4, pp. 704-719.
    5. Shao, J., 2000: Fuzzy Categorization of Weather Conditions for Thermal Mapping. Journal of Applied Meteorology: Vol. 39, No. 10, pp. 1784-1790.
    6. Lakshmanan, V., 2000: Using a Genetic Algorithm to Tune a Bounded Weak Echo Region Detection Algorithm. Journal of Applied Meteorology: Vol. 39, No. 2, pp. 222-230.
    7. Liu, Hongping, V. Chandrasekar, 2000: Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification. Journal of Atmospheric and Oceanic Technology: Vol. 17, No. 2, pp. 140-164.
    8. Santamouris, M., G. Mihalakakou, B. Psiloglou, G. Eftaxias, D. N. Asimakopoulos, 1999: Modeling the Global Solar Radiation on the Earth's Surface Using Atmospheric Deterministic and Intelligent Data-Driven Techniques. Journal of Climate: Vol. 12, No. 10, pp. 3105-3116.
    9. Walmsley, John L., William R. Burrows, Robert S. Schemenauer, 1999: The Use of Routine Weather Observations to Calculate Liquid Water Content in Summertime High- Elevation Fog. Journal of Applied Meteorology: Vol. 38, No. 4, pp. 369-384.
    10. Vivekanandan, J., S. M. Ellis, R. Oye, D. S. Zrnic, A. V. Ryzhkov, J. Straka, 1999: Cloud Microphysics Retrieval Using S-band Dual-Polarization Radar Measurements. Bulletin of the American Meteorological Society: Vol. 80, No. 3, pp. 381- 388.
    11. Talbot, Lisa M., Bryan G. Talbot, Robert E. Peterson, H. Dennis Tolley, Harvey D. Mecham, 1999: Application of Fuzzy Grade-of-Membership Clustering to Analysis of Remote Sensing Data. Journal of Climate: Vol. 12, No. 1, pp. 200-219.
    12. Cornman, Larry B., Robert K. Goodrich, Corinne S. Morse, Warner L. Ecklund, 1998: A Fuzzy Logic Method for Improved Moment Estimation from Doppler Spectra. Journal of Atmospheric and Oceanic Technology: Vol. 15, No. 6, pp. 1287-1305.
    13. Baum, Bryan A., Vasanth Tovinkere, Jay Titlow, Ronald M. Welch, 1997: Automated Cloud Classification of Global AVHRR Data Using a Fuzzy Logic Approach. Journal of Applied Meteorology: Vol. 36, No. 11, pp. 1519-1540.
    14. Baum, B.A., T. Uttal, M. Poellot, T.P. Ackerman, J.M. Alvarez, J. Intrieri, D.O'C. Starr, J. Titlow, V. Tovinkere, E. Clothiaux, 1995: Satellite Remote Sensing of Multiple Cloud Layers. Journal of the Atmospheric Sciences: Vol. 52, No. 23, pp. 4210-4230.
    15. Hagelberg, Carl, Jason Helland, 1995: Thin-Line Detection in Meteorological Radar images Using Wavelet Transforms. Journal of Atmospheric and Oceanic Technology: Vol. 12, No. 3, pp. 633-642.
    16. Harr, Patrick A., Russell L. Elsberry, 1995: Large-Scale Circulation Variability over the Tropical Western North Pacific. Part II: Persistence and Transition Characteristics. Monthly Weather Review: Vol. 123, No. 5, pp. 1247-1268.
    17. Harr, Patrick A., Russell L. Elsberry, 1995: Large-Scale Circulation Variability over the Tropical Western North Pacific. Part I: Spatial Patterns and Tropical Cyclone Characteristics. Monthly Weather Review: Vol. 123, No. 5, pp. 1225-1246.
    18. Seaver, William L., James E. Lee, 1987: A Statistical Exmination of Sky Cover Changes in the Contiguous United States. Journal of Applied Meteorology: Vol. 26, No. 1, pp. 88-95.


    Mahbub Hasan
    Foreign Faculty Member
    Miyagi National College of Technology
    email: hasan@miyagi-ct.ac.jp

    I have 3 published papers on application of fuzzy set theory for controlling of irrigation water. The titles and the publishing Jornal and Transactions are given below:

    1. Development of an operation model for intake flow size control of irrigation using fuzzy set theory. Mahbub Hasan, MATSUI Hiroyuki, MIZUTANI Masakazu and GOTO Akira. Published in the Transactions of the Japanese Society of Irrigation, Drainage, and Reclamation Engineering No. 216(69-6), Dec 2001. pp. 2532.
    2. Parameterization technique for optimization production rule table in fuzzy inference model. Mahbub Hasan, GOTO Akira,MIZUTANI Masakazu and MATSUI Hiroyuki. Published in the Transactions of the Japanese Society of Irrigation, Drainage, and Reclamation Engineering No. 191 (65-5), Oct 1997. pp. 1522.
    3. A model for determination of intake flow size. Development of optimum operational method for irrigation using fuzzy set theory (1). Mahbub Hasan, MIZUTANI Masakazu, GOTO Akira and MATSUI Hiroyuki. Published in the Journal of the Japanese Agricultural System Society, Vol. 11. No. 1, April 1995.

    Lotfali Agheli Kohnehshahri
    Economic Research Center
    Tarbiat Modarres University
    P.O.Box 14115-316
    Tehran, Iran
    email: Lagheli@hotmail.com

    I have estimated "Green GNP" and the "sustainability degree of Iranian economy". I can send my dissertation abstract for you.

    Dave Lam
    NHRI Burlington

    RAISON AI hydrology model

    Reza Langari
    Assistant Professor
    Department of Mechanical Engineering
    Texas A&M University
    College Station, TX
    email: langari@arya.tamu.edu

    We have done some work in environmental factors in engineering design using F/L. Let me know if you need further information.

    Walter Maner
    Faculty, Department of Computer Science
    Bowling Green State University
    Bowling Green, Ohio
    email: maner@cs.bgsu.edu

    "WXSYS: Weather Lore + Fuzzy Logic = Weather Forecasts"

    Why try to improve on official forecasts?
    Why use fuzzy logic to predict the weather?
    Why turn to "weather lore" as a source for fuzzy rules?
    What is included in our forecasts?
    How was "weather lore" used to build forecast rules?
    How does WXSYS work overall?
    Why is user input confined to cloud observations?
    How does one use WXSYS?
    Does WXSYS make valid forecasts?
    What are our plans for WXSYS?

    Directory containing: FuzzyCLIPS program, sample test data, and miscellaneous accompanying files and sample test data

    Jacques Marcoux
    email: jmarcoux@ec.gc.ca

    The latest AMS conference on Weather Analysis and Forecasting had a session on AI so you might want to check the pre-print.

    Shreedhar Maskey
    Ph.D. Research Fellow
    Hydroinformatics and Knowledge Management
    P.O. Box 3015
    2601 DA Delft
    The Netherlands
    email: maske@ihe.nl

    I am doing research on "Modelling Uncertainty in Flood Forecasting and Warning Systems" for my PhD. I am using probabilistic and fuzzy approaches and the combination of the two to model quantitative and qualitative uncertainty in real time flood forecasti ng. Particularly in fuzzy approach I published one paper:

    Maskey, S. (2001). "Uncertainty Analysis in Flood Forecasting and Warning System Using Expert Judgement and Fuzzy Set Theory". In Safety & Reliability, Zio, E., Demichela, M. and Piccinini, N. (eds.), pp. 1787-1794.

    And one is under preparation (intended for a Journal), with a provisional title:

    "Quantitative Vs. Qualitative Uncertainty in Flood Forecasting and Warning".

    Some other interesting references I found are:

  • Ayyub, B.M. (1998). Uncertainty Modelling and Analysis in Civil Engineering, CRS Press.
  • Klir, G.J. and Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic, Theory and Applications. Prentice Hall PTR.
  • Ross, T.J. (1995). Fuzzy Logic with Engineering Applications. McGraw-Hill, Inc., USA.
  • Sundararajan, C.R. (1994). "Uncertainties in piping frequency analysis". Fuzzy Sets and Systems, Vol. 66, pp. 283-292.
  • Tsoukalas, L.H. and Uhrig, R. E. (1997). Fuzzy and Neural approaches in Engineering. John Wiley & Sons, Inc., New York.

  • Geoffrey Matthews
    School of Computer Science
    Western Washington University
    email: matthews@cs.wwu.edu

    Applied AI techniques to classify biota responses to stressors.

    Miha Mraz
    Faculty of Computer and Information Science
    Trzaska 25, 1000 Ljubljana
    Slovenia, Europe
    email: mraz@kri.fri.uni-lj.si

    I'm working on my PhD degree in the field of fuzzy cellular automata and the real case study is from the field of fire spread in nature environment. From this field me and my colleagues had two references:

    [1] Mraz M., Zimic N., Virant J.: Computer aided predicting wind driven wild land fire size and shape using fuzzy logic, 9th International Symposium Computer Science for Environment Protection, Berlin, September 1995, Germany (p.726..732)

    [2] M.Mraz, N.Zimic, J.Virant: Predicting Wind Driven Wild Land Fire Shape using Fuzzy Logic in Cellular Automata. - V: Proc. of IX Intern. Symposium on Artificial Intelligence and VI.Intern. Conference on Industrial Fuzzy Control and Intelligent Systems, Texas A&M University, Cancun, Mexico, Nov.1996, accepted for presentation;

    Please contact me if this area of application is interested for you.

    Jim Murtha
    Meteorologist, Environment Canada
    Edmonton, Alberta
    email: jim.murtha@ec.gc.ca


    Jim Murtha, 1995: Applications of Fuzzy Logic in Operational Meteorology, Scientific Services and Professional Development Newsletter, Canadian Forces Weather Service, 42-54.


    The topic of fuzzy logic as a decision-making technique is introduced. It is recommended that applications of this technique could be effectively applied in the area of operational meteorology. An example of such an application, the determination of the probability of formation of radiation fog, is discussed and examples of the method are presented. Other possible meteorological applications are suggested. Additionally, a software package which aids in the development of such applications is briefly described.

    Thomas Nehrkorn
    Cambridge, MA
    email: trn@aer.com

    MIT's Lincoln Laboratory has developed some algorithms for the FAA (for terminal weather nowcasting and forecasting) based on AI. See the Weather Sensing Group and Air Traffic Control Automation pages at the MIT Lincoln Lab.

    Here are some references (you can also check the FAA page and look for info on the ITWS project (recently awarded to Raytheon) for technical info).

    1. MBD: Microburst Detection

    1.1. T. J. Dasey. A Shear-Based Microburst Detection Algorithm for the Integrated Terminal Weather System (ITWS). Proc. 26 th Intl Conf. on Radar Meteorology, American Meteorological Society (May, 1993).

    1.2. M. P. Matthews, T. J. Dasey. Improving Aircraft Impact Assessment with the Integrated Terminal Weather System Microburst Detection Algorithm. Proc. Fifth Intl. Conf. on Aviation Weather Systems, American Meteorological Society (August, 1993).

    2. MBPredict: Microburst Prediction

    2.1. M.M. Wolfson, R.L. Delanoy, B.E. Forman, R.G. Hallowell, M.L. Pawlak, P.D. Smith. Automated Microburst Wind Shear Prediction. Linc. Lab. J. 7 (1994).

    2.2. R.L. Delanoy and J. Verly. Computer apparatus and method for fuzzy template shape matching using a scoring function. U.S. Patent No. 5,222,155 (June 1993).

    2.3. R.L. Delanoy, J.G. Verly, and D.E. Dudgeon. Functional Templates and Their Application to 3-D Object Recognition. Proc. Intl. Conf. on Acoustics, Speech, and Signal Processing (ICAASP) (March 1992).

    2.4. R.L. Delanoy, J.G. Verly, and D.E. Dudgeon. Pixel-Level Fusion Using Interest Images. Technical Report 979, Lincoln Laboratory (1993).

    3. MBMerge: Microburst Prediction and Detection Merging

    3.1. M.M. Wolfson, R.L. Delanoy, B.E. Forman, R.G. Hallowell, M.L. Pawlak, P.D. Smith. Automated Microburst Wind Shear Prediction. Linc. Lab. J. 7 (1994).

    4. MIGFA: Machine Intelligent Gust Front Algorithm

    4.1. Integrated Terminal Weather System (ITWS) Algorithm Description: Terminal Winds Chapter. Technical Report ATC-225, DOT/FAA/ND-95-11, MIT Lincoln Laboratory, Lexington, MA, June 1995.

    4.2. Richard L. Delanoy, Jacques G. Verly, and Dan E. Dudgeon. Functional templates and their application to 3-D object recognition. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), San Francisco, CA, March, 1992.

    4.3. Richard L. Delanoy and Seth W. Troxel. Automated gust front detection using knowledge- based signal processing. In IEEE 1993 National Radar Conference, Boston, MA, April 1993.

    4.4. Richard L. Delanoy and Seth W. Troxel. Machine intelligent gust front detection. Lincoln Laboratory Journal, 6(1), Spring 1993.

    4.5. Richard L. Delanoy , J. G. Verly, and D. E. Dudgeon. Pixel-level fusion using interest images. Technical Report TR-979, MIT Lincoln Laboratory, Lexington, MA, May 1993.

    4.6. S. W. Troxel and R. L. Delanoy. Machine intelligent approach to automated gust front detection for Doppler weather radars. In SPIE Vol. 2220: Image Sensing, Processing, and Understanding for Control and Guidance for Aerospace Vehicles, Orlando, FL, April 1994.

    4.7. S. W. Troxel and R. L. Delanoy. Machine intelligent gust front detection for the Integrated Terminal Weather System (ITWS.). In Sixth Conference on Aviation Weather Systems, Dallas, TX, January 1995.

    5. GFUP: Gust Front Update

    5.1. Richard L. Delanoy and Seth W. Troxel. Automated gust front detection using knowledge- based signal processing. In IEEE 1993 National Radar Conference, Boston, MA, April 1993.

    5.2. S. W. Troxel and R. L. Delanoy. Machine intelligent approach to automated gust front detection for Doppler weather radars. In SPIE Vol. 2220: Image Sensing, Processing, and Understanding for Control and Guidance for Aerospace Vehicles, Orlando, FL, April 1994.

    Franz Newland, Astronautics Group
    Department of Aeronautics and Astronautics
    Department of Electronics and Computer Science
    University of Southampton
    Southampton, UK
    email: ftn@soton.ac.uk

    Click our faculty link to see the abstract of some current work being undertaken in the department of Aeronautics and Astronautics on the use of Fuzzy Logic and Neural Networks for cloud motion analysis. I will send you the paper should you be interested further. I have just finished the first year of my research, and am keen to have some comments and feedback on my work.

    A new European project called FLIERS is about to start in the department. This will be looking at using fuzzy logic for analysis of land use. This is a collaborative effort from universities across Europe, and has attracted a sizable EC grant.

    In addition, a new research studentship has just started this week on fuzzy coastal classification.

    I hope our work is of interest to you. Our group at Southampton would be very interested in possible contributions to this, and would like to know more, in any instance.

    Masoud Nikravesh
    Chair, BISC Special Interest Group in Earth Sciences
    Earth Sciences Division, MS 90-1116
    Lawrence Berkeley National Laboratory
    University of California at Berkeley
    Berkeley, CA
    email: nikraves@cs.berkeley.edu or MNikravesh@lbl.gov

    Berkekey Initiative In Soft Computing (BISC)
    Special Interest Group in Earth Science

    Pattern Recognition
    Concurrent Information Processing (CIP)
    Soft Computing
    Neural Network
    Fuzzy Logic
    Genetic Algorithm
    Probabilistic Reasoning
    Modern Control Theory
    Model Predictive Control, Optimal Control, Adaptive Control
    Applied Process Control Design and Computer Simulation
    Nonlinear Process Control - State Space Model, Geometrical Control
    Data Fusion and Mining
    Integration of Soft Computing and Earth Sciences Techniques
    Broadly, Earth Sciences subsumes but is not limited to the following areas;
    Geophysics (Seismology and electromagnetic)
    Borehole wireline log evaluation
    Reservoir Engineering
    Mineral Prospecting
    Environment Risk Assessment
    Nuclear Waste Storage Risk Assessment.

    Fact Inc. deals with: remote sensing, pattern and image recognition, soft computing, artificial intelligence, environmental control/remediation, earthquake analysis, optimization, control theory and robotics.

    Ulf Nordlund
    Institute for Earth Sciences
    Uppsala University, Sweden
    email: ulf.nordlund@pal.uu.se

    There's a simulation program using a user-defined fuzzy system for controlling deposition and erosion of sediments at an ocean margin...

    You can download the program, manual and test files from URL:
    Let me know if you need some published documentation.

    Nordlund, U., 1996: "Formalizing geological knowledge - with an example of modeling stratigraphy using fuzzy logic", Journal of Sedimentary Research, 66:4, pp. 689-698

    Richard Podolsky, Ph.D.
    Avian Systems
    95 Magnolia Ave.
    Tenafly, NJ 07670
    Phone: 201-568-9494
    email: podolsky@att.net

    I am an ecologist who has written several image mining, image understanding software programs that employ fuzzy algorithms. In particular, the image mining application FullPixelSearch uses several fuzzy routines.

    Biodiversity Book

    Oleg M. Pokrovsky
    Main Geophysical Observatory
    Karbyshev str.7
    St.Petersburg, 194021

    Fuzzy logic approach for description of meteorological impacts on urban air pollution species: A Hong Kong case study, Oleg M. Pokrovsky, Roger H.F. Kwok and C.N. Ng


    The aim of the present paper is to develop an alternative approach to conventional dynamic and photochemical models for operational short-term forecasting of urban air pollutants. It is well known that there are some practical difficulties which prevent necessary progress in the development of these models as a forecasting tools. A fuzzy logic based method has been developed here to study the impact of meteorological factors on the evolution of air pollutant levels and to describe them quantitatively. This method meets all requirements but requires quite substantial amount of observational data. The developed model bases on simulation of diurnal cycles of principal meteorological variables (wind speed and direction, solar irradiance, and air temperature) and the corresponding diurnal patterns of various air pollutants (O3, NO2, NO, Noy). In addition, the spatial patterns of these parameters are also studied. Both temporal and spatial parameter distributions have been considered in order to investigate impacts of meteorological factors, and they are incorporated in the models as state vect ors in the multidimensional space. Here we suggest that most of the weather and air pollution phenomena could be simulated by sequences of its conservation inside some fuzzy sets and the transition from one fuzzy set to another. Therefore, important key point here is the development of the transition rules.


    Potsdam Institute for Climate Impact Research
    Potsdam, Germany

    Mission of the Potsdam Institute for Climate Impact Research

    At the end of the second millennium, science is facing a new challenge of unprecedented complexity: the causes, mechanisms and potential impacts of global environmental changes which are already taking place have to be analyzed, and ways towards a sustainable balance between nature and civilization must be found.

    Aware of the importance of these problems and the urgency of taking precautions, the Minister for Science, Research and Culture of the Land of Brandenburg and the Federal Minister for Research and Technology have decided to follow the advice of the Science Council of Germany and to jointly create a dedicated Global Change research institute.

    The Potsdam Institute for Climate Impact Reserach (PIK) was founded in 1992. Its mission is to investigate the consequences of global climate change as a paradigm for the unfolding fundamental crisis between the ecosphere and humanity. Particular attention will be placed on regional aspects such as the consequences of changed rainfall pattern for Central European agriculture and forestry, or the threat to German coastal zones caused by sea level changes as well as increased frequency and amplitude of storms.

    In the medium run, however, a "holistic perspective" of System Earth based upon integrated models will be indispensable due to the intensive long-range interactions between atmosphere and other natural spheres (hydrosphere, biosphere, pedosphere, etc.). This also applies to feedbacks between climate change and the anthroposphere (economic, social and political reactions such as change in land-use or provisions to reduce greenhouse gas emission; please consult the diagram).

    The scope of these research tasks requires the coupling of disciplines (ranging from meteorology to environmental psychology), scales (ranging from local to global, from individual to international), and motives for research (ranging from the desire to obtain basic knowledge to the demand for political strategies of sustainable development). This demands high integrative capabilities of all members of the institute.

    For more details, go to the Potsdam Institute for Climate Impact Research web page.

    Thirumalainambi Rajkumar
    Department of Environmental Science and Policy
    University of California, Davis
    email: rajnambi@ucdavis.edu

    Research Interests:

  • Application of fuzzy logic to environmental problems
  • Application of fuzzy logic in environmental risk assessment of pollution
  • Neural networks and hybrid neural networks


    1. T.Rajkumar and Hans W. Guesgen, 1996: Fuzzy Exposure Model PP1051-1055 IEEE SMC, 1996, Beijing
    2. T.Rajkumar and Hans. W. Guesgen, 1997: Determination of Chemical exposure using multilayer feed forward neural networks, PP264-267, IASTED Conference, 1997, Banff, Canada
    3. T.Rajkumar, Hans W. Guesgen, 1997: Analysis of chemical exposure through inhalation using hybrid neural network, PP311-316 IEEE SMC 1997, Orlando, USA
    4. T.Rajkumar, Hans. W. Guesgen, S.Robinson and G.W.Fisher, 1999: A new dose model for assessment of health risk due to contaminants in air, Journal of Air and Waste Management, September 1999
    5. T. Rajkumar and Hans W. Guesgen, 1999: A hybrid approach to risk assessment for multiple pathway chemical exposures, PP127-130, AAAI Spring Symposium, Predictive toxicology of chemicals: Stanford University, USA , March,1999
    6. T.Rajkumar, Hans W. Guesgen and Des Gorman, 1999: Estimating the consumption of Tap water using fuzzy concepts, CIMA'99 International Congress on Computational Intelligence: Methods and Applications Rochester, New York, USA June 22-25, 1999

    Two more papers are under preparation during my Post-doc

  • Salinity prediction of San Fransico Bay area using neural network
  • Population viability analysis of Clapper Rail using fuzzy logic

    Mack Ramachandra
    email: MRAMACHANDRA@vax.clarku.edu

    IDRISI (click on case studies section) Clark Univ. GIS software has a routine that allows you to fuzzify any environmental variable (express the variable as a membership function to any set). We recently finished an application of it to vulnerability study and its on the web, in case you are interested I think that also has some references to some of the work done here.

    Keith M. Reynolds
    Pacific Northwest Research Station
    Corvallis Forestry Sciences Lab
    3200 SW Jefferson Way
    Corvallis, OR 97331
    Phone: 541-750-7434
    FAX: 541-750-7329
    email: reynoldsk@fsl.orst.edu

    The USDA Forest Service Pacific Northwest Research Station in Corvallis, Oregon, has developed the Ecosystem Management Decision Support (EMDS) system. The system developed by Dr. Reynolds and colleagues integrates the logical formalism of knowledge based reasoning into a GIS environment to provide decision support for ecological landscape assessment and evaluation. The knowledge based reasoning schema of EMDS uses an advanced object- and fuzzy logic-based propositional network architecture for knowledge representation. The basic approach affords several advantages over more traditional forms of knowledge representations such as simulation models and rule based expert systems.

    The system facilitates evaluation of complex, abstract topics such as forest type suitability that depend on numerous, diverse subordinate conditions because EMDS is fundamentally logic based. The object-based architecture of EMDS knowledge bases allows incremental, evolutionary development of complex knowledge representations. Modern ecological and natural resource sciences have developed numerous mathematical models to characterize very specific relations among ecosystem states and processes. However, it is far more typical that knowledge of ecosystems is more qualitative in nature. Approximate reasoning, as implemented in fuzzy logic, significantly extends the capability to reason with the types of imprecise information typically found in natural resource science. Finally, the propositional network architecture of EMDS knowledge bases allows both the ability to evaluate the influence of missing information and the ability to reason with incomplete information.

    The first production version of the system was released in February 1997. The USDA Forest Service, in cooperation with other federal and State partners, is currently applying the EMDS system in three ecoregional assessments now underway in the US. Since its initial release, EMDS has been requested by 70 natural resource institutions worldwide, including 18 universities, 32 national research and/or management organisations, 10 state/province level organisations, and 5 international research institutes.

    David W. Roberts
    Utah State University
    Logan, Utah
    email: dvrbts@nr.usu.edu
    Roberts, D.W. 1986. Ordination on the basis of fuzzy set theory. Vegetatio 66: 123-131.
    Roberts, D.W. 1989. Fuzzy systems vegetation theory. Vegetatio 83: 71-80.
    Roberts, D.W. 1989. Analysis of forest succession with fuzzy graph theory. Ecological Modelling 45: 261-274.

    Clare Robinson
    Department of Automatic Control and Systems Engineering
    University of Sheffield, Sheffield, U.K.
    email: C.A.Robinson@Sheffield.ac.uk

    We have recently completed some work on frost prediction using neural networks and have a paper accepted for publication in a journal 'Computers and Electronics in Agriculture.' This uses meteorological data collected from Sicily over 7 years. This work has also been extended to form a fuzzy neural system where the meteorological data is encoded into fuzzy membership values before being input to the neural network, and the outputs are defuzzified to give a prediction. Further work is intended which will include studying the trained neural network to deduce a rule base for a meteorological system.

    Robinson, C., and Mort, N. - "A neural network system for the protection of citrus crops from frost damage", Computers and Electronics in Agriculture, 16, 1997, pp. 177-187

    Dr. Arkadiusz Salski
    Ecosystem Research Center, University of Kiel
    Kiel, Germany
    email: arek@pz-oekosys.uni-kiel.d400.de

    Since 1988, I have been working in fuzzy sets and fuzzy logic applications in ecology, particularly in data analysis and knowledge-based modelling. A fuzzy set and fuzzy logic approach can be used to handle some problems of uncertainty in ecological research (uncertainty of ecological data and uncertainty of expert knowledge). Compared to the conventional methods of information processing the application of the fuzzy set theory enables us to make better use of imprecise data and vague knowledge. This means here:
    1. the representation and processing of imprecise data in the form of fuzzy sets;
    2. the representation and processing of vague knowledge in the form of linguistic rules with imprecise terms defined as fuzzy sets.
    In order to facilitate the data analysis and the construction of fuzzy knowledge-based models of ecosystems we (me and a group of Master-Thesis Students from the Inst. of Computer Science, research group of Prof. Kandzia) have developed three support systems, namely:
    1. Fuzzy Clustering System ECO-FUCS /present application:
      Fuzzy Clustering of Existing Chemicals According to their Ecotoxicological Properties
      (Friederichs et al., 1996, Ecological Modelling, v.85, No.1, pp.27-40);
    2. Fuzzy Evaluation and Kriging System FUZZEKS application example: the spatial interpolation of hydrogeological data. Piotrowski et al., 1996, Glacial Aquitard Thickness in Northwestern Germany, Based on Fuzzy Kriging, Mathematical Geology, v. 28, No.4, pp.437-452);
    3. Modelling Support System based on Fuzzy Logic FLECO; FLECO employs fuzzy logic to handle inexact reasoning and fuzzy sets to handle uncertainty of data /application examples: a fuzzy knowledge-based model of the annual production of Skylarks (Daunicht et al., 1996, Ecological Modelling, v.85, No.1, pp. 67-74); and and a fuzzy model of the population dynamics of the Yelow-necked mouse in a beech forest (in press).
    In October'93, I organised the workshop "Fuzzy Logic in Ecological Modelling" in Kiel. This workshop was the first meeting on fuzzy logic in ecological research in Europe with 51 participants from six countries. We succeeded in bringing together not only ecologists but also mathematicians, computer scientists and engineers who are active in this field. The workshop program contained 14 papers which focused on fuzzy data analysis, fuzzy modelling / expert systems, fuzzy decision making and engineering applications. The workshop proceedings have been published in: Salski A., Fraenzle O. & Kandzia P. (eds), special issue of Ecological Modelling, 1996, v.85, No.1.

    The last years, I have organised some "fuzzy-ecology" sessions within the framework of several conferences. You can find some interesting papers e.g. in the proceedings of the FUZZY'96 Conf. in Zittau (Germany).

    Marc Sarazin
    email: msarazin@eso.org

    We did quite a lot of work on that topic for the prediction of astronomical observing conditions at ESO observatories, the references and papers are available on the WEB at the Seeing Prediction, Weather Forecasting page.

    Dr.-Ing. Matthias Schneider
    sje Schneider & Jorde Ecological Engineering
    Viereichenweg 12
    70569 Stuttgart (Vaihingen)
    email: mailbox@sjeweb.de

    During my time at the Institute of hydraulic engineering at the University of Stuttgart in my PhD thesis I have developped a new module of the simulation model CASIMIR (computer aided simulation system for instream flow requirements) dealing with fish habitat prediction. The module is based on fuzzy rules given by fish experts. The used fuzzy sets for the relevant physical parameters describing river structures and hydraulics can be adapted to different river types. references are:

  • SCHNEIDER, M. (2001): Habitat- und Abflussmodellierung für Fließgewässer mit unscharfen Berechnungsansätzen. (Modelling flow an habitat in running waters using fuzzy approaches) - Dissertation, Mitteilungen des Instituts für Wasserbau, Heft 108, Universität Stuttgart, Eigenverlag, Institut für Wasserbau der Universität Stuttgart, Stuttgart;
  • SCHNEIDER, M., K. JORDE, F. ZÖLLNER & F. KERLE (2001): Development of a user-friendly software for ecological investigations on river systems, integration of a fuzzy rule-based approach, proceedings Environmental informatics 2001, 15th International Sympo-sium, Informatics for Environmental Protection, 2001, ETH Zurich, Switzerland;
  • JORDE, K., M. SCHNEIDER & F. ZÖLLNER (2000): Analysis of Instream Habitat Quality - Preference Functions and Fuzzy Models. Stochastic Hydraulics 2000, Wang & Hu (eds.), Balkema, Rotterdam, pp. 671 - 680.
  • Prof. Bardossy (also Institute of Hydraulic Engineering, University of Stuttgart) uses fuzzy-rule-based logical approaches for very different environmental issues, so as climatology, meteorology (rainfall), soil contamination, algae growth, regulation of catchment systems and many more. A good (though theoretical) reference is the book:

  • BARDOSSY, A.; DUCKSTEIN, L. (1995): Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems.- CRC Press, Boca Raton, New York, London, Tokio.

  • Haresh C. Shah
    Earthquake Engineering Center
    Department of Civil Engineering
    Stanford University
    email: shah@cive.stanford.edu

    Used fuzzy logic to help predict the occurrence of earthquakes. Interest is to develop strategies for natural hazards risk management on a global scale. He is also working on developing financial and insurance risk management strategies for catastrophic events such as earthquakes, hurricanes, etc.

    Dr. William Silvert
    Instituto de Investigação das Pescas e do Mar
    Departamento de Ambiente Aquático
    Avenida de Brasília, s/n
    1449-006 Lisboa, Portugal
    email: wsilvert@ipimar.pt

    Fuzzy Logic and Ecological Indices

    Modelling muddy data with fuzzy logic ...

    "The logs, which although obtained from sites in the Gulf of Aqaba are typical of the kind of data available for the Bay of Fundy and other areas where mariculture is common, pose several problems for scientific analysis. They include many different kinds of observations, some of which are qualitative or simple presence-absence observations, and the quality of the data was compromised by the need to use a team of divers with varying levels of scientific background. Crucial observations were often omitted, especially at the beginning of the monitoring program when the experimental protocols were not fully developed.

    "The focus of the present project was on the use of fuzzy logic to characterize the data and to provide a methodology for extracting useful results from the data set. Fuzzy logic is ideally suited for the analysis of qualitative and verbal data, but it also allows for the inclusion of quantitative data, and can easily incorporate nonlinear relationships that are difficult to describe statistically. The original plan was to develop a preliminary analytic technique and to use it for the analysis of a few dozen logs. Despite unexpected computational difficulties due to the novelty of the technique, a complete time series of approximately 150 logs was processed and the original protocol went through several levels of revision which significantly improved the descriptive power of the analysis.

    Ron Spiegel
    Environmental Protection Agency
    Research Triangle Park, NC
    email: rspiegel@engineer.aeerl.epa.gov

    Epa is conducting research in fuzzy-logic-based control systems in the following areas:
    1. control of AC induction motors for energy optimization and performance enhancement;
    2. control of wind turbines to maximize output power and provide robust speed control to reduce the effects of wind gusts and turbine oscillatory torque; and (3) control of small boilers for NOx reduction.
    Additionally, we are using neural networks to reduce transient emissions from rotary kiln incinerators. If you want more information about these projects, write me.

    Jean-Philippe Steyer
    Research Group in WWTPs Instrumentation, Control and Diagnosis
    Laboratory of Environmental Biotechnology

    Avenue des Etangs
    11100 Narbonne - France
    email: steyer@ensam.inra.fr

    I manage a research group involved in the modeling, control and diagnosis of biological wastewater treatment plants and fuzzy logic is one of our main topics. We regard it as a method that can enhance other techniques (classical control, neural networks, ...), as stated at the top of this webpage. One PhD was already defended on the fuzzy control of an anaerobic digestion process and one will be defended by the end of 1999 concerning the fuzzy fault detection and isolation.

    Dr. Molly Stock,
    University of Idaho
    email: mstock@uidaho.edu

    Editor of AI Applications for natural resources, agriculture, and environmental science.
    Volume 9, nos. 1 & 2, 1995
    Volume 5, no. 4, 1991
    Volume 1, no. 3, 1987

    and these may soon be available (?):
    Volume 10, no. 3, 1996
    Volume 10, no. 2, 1996
    Volume 10, no. 1, 1996

    Kunio Takezawa, Ph.D.
    Research Information Section
    Hokuriku National Agricultural Experiment Station, Japan
    email: takezawa@inada.affrc.go.jp

    I would be very glad if my WWW page helps you. I am pleased to send you related reprints of my papers if you need.

    Ramesh Teegavarapu
    Facility for Intelligent Decision Support
    Dept. of Civil Engineering
    University of Manitoba
    Winnipeg, Manitoba, Canada R3T 5V6
    email: rame@ce.umanitoba.ca or umteegav@cc.umanitoba.ca

    I am working in the areas of neural networks and fuzzy set applications to water resources management. Applications include modelling decision maker's preferences using in membership functions for real-time operation of reservoirs. NN are used for both reservoir operations as well as hydrologic time-series forecasting (rainfall and streamflow)

    V. Torra
    email: vtorra@etse.urv.es

    I have a paper on the determination of the radius of influence of a trap to capture saurians. I do not know if that fits to your interests. I send below the abstract of this article. I can send you a copy if you are interested.


    V. Torra, Synthesis of membership functions to determine a radius of influence, Mathware, 2 (1995) 47-60.


    In this work we study the synthesis of membership functions when they have been calculated from a set of observations according to the definition of (Zhang, 1993). The results obtained have been used to determine the radius of influence of a tramp for Podarcis lilfordi (a kind of saurians) in the island of Cabrera (Balearic Islands). The measure of this radius was used in a later work to estimate the density of these saurians in the island.

    Liem Tran
    Department of Geography
    University of Hawaii at Manoa
    2424 Maile Way, Porteus 414
    Honolulu, Hawaii 96822
    Tel: (808) 956-7156, Fax: (808) 956-3512
    email: tliem@hawaii.edu

    I am a PhD candidate at Geography Department, University of Hawaii. My dissertation topic is to use multiobjective fuzzy regression to improve the performance of the Revised Universal Soil Equation. I' m currently [Nov. 1998] at the final stage in completing my dissertation.

    Richard Wagoner
    Research Applications Program
    National Center for Atmospheric Research
    Boulder, Colorado
    email: wagoner@rap.ucar.edu

    We use fuzzy logic in about 90% of the systems we build these days. We are currently in the process of writing a book on the subject but this will not be available for another 18-24 months [~late 2004].

    Here is a presentation about post-processing data fusion technology at NCAR:


    Here is a list of the reference materials that we use frequently:

  • Chi Z., H. Yan and T. Pham 1996: Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition, World Scientific pp.225. This book has many useful references and examples of how to use fuzzy logic in pattern recognition.
  • Cox, Earl, 1998. The Fuzzy Systems Handbook 2nd Edition, AP Professional, Academic Press, San Diego, CA, 716 pp. Very practical engineering approach to applications of the theory.
  • Klir, G.J. and T.A. Folger, 1988. Fuzzy Sets Uncertainty and Information, Prentice Hall, 355pp.
  • Klir, G.J. and B. Yuan, Eds., 1996. Fuzzy Sets, Fuzzy Logic and Fuzzy Systems. Selected papers of Lofti Zadeh. World Scientific Publishing Company, 826 pp. This one has many of the works of Zadeh who founded the field. It is interesting to read the early papers and see the evolution of the theory.
  • Kosko, Bart, 1993. Fuzzy Thinking, Hyperion, New York, 300 pp. Excellent introductory text to the theory. Kosko is the leading guru on fuzzy theory these days. He sometimes goes off the deep end with his zen connectivity, but if you can put up with this, the text is fun to use and teaches you a lot about how the theory emerged.
  • Kosko, Bart, 1997. Fuzzy Engineering, Prentice Hall. Much more technical than Fuzzy Thinking, but not as well written.
  • Mendel, Jerry M., 1995. "Fuzzy Logic Systems for Engineering: A Tutorial", Proceedings of the IEEE, vol. 83, no. 3, March 1995, pp. 345-377.
  • Ross, Timothy J., 1995. Fuzzy Logic with Engineering Applications, McGraw-Hill, New York, 600 pp. A very good book for applications folks, but unfortunately I think it may be out of print
  • Zadeh, L.A., 1965. "Fuzzy sets," Information and Control, vol. 8, pp. 338-353, 1965. The original paper - pretty heavy mathematical theory.
  • L.A. Zadeh, L.A., 1975. "The concept of linguistic variable and its application to approximate reasoning," Information Sciences, vol. 8, pp. 199-249. and vol. 9, pp. 43-80, 1975. This is a favorite paper for many folks who use fuzzy theory.

  • Zaheeruddin Zaheeruddin
    Department of Electrical Engineering
    Faculty of Engineering and Technology
    Jamia Millia Islamia (A Central university)
    New Delhi-110025, Fax: 91-11-26982651
    email: zaheer_2k@hotmail.com

    I have done my Ph.D. thesis entitled "A Soft Computing Model for Noise Pollution" at the School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India. One paper entitled "Fuzzy Modelling of Human Work Efficiency in Noisy Environment" has been accepted for presentation at The IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2003) to be held during May 25-28, 2003 at St. Louis, USA and will be pulished in the Proceedings of the same. Three other papers have been submitted for possible publication in IEEE Transactions.

    Mahdi Zarghaami
    Ph.D. Candidate on Water Resources Management
    Civil Engineering Department
    Sharif University of Technology
    P.B.: 11365-9313
    email: zarghaami@yahoo.com

    I work on fuzzy multi-criteria decision making for water resources management.

    Slobodan P. Simonovic has very interesting files in this area, that you can download from his homepage.