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.
Research assistant at the Computer Science Department
of the Christian-Albrechts-Universität Kiel, Germany.
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
A complete list of my publications (some of them available as postscript files) is
Flexible Intelligence Group, LLC
Tuscaloosa, AL, USA
We are in the process of applying fuzzy logic to agriculture and fisheries.
Bachisio Arca, F. Benincasa, Matteo De Vincenzi
Institute for Agroecosystem Monitoring
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.
- Neural nets to estimate radiative fluxes (net solar radiation, diffuse
- Thermodynamic compensation of ultrasonic range finder using a neural
- Estimate Pan evaporation (A class) using a neural network
- Planning neuronal model to predict night minimum temperature
Naval Research Laboratory
MEDEX: fuzzy rule-based system for predicting the onset and cessation of gale force winds.
- cloud classification
- artificial intelligence in environmental science
Dr. Andras Bardossy
University of Stuttgart, Germany
Has written numerous papers and at least one book on this subject,
especially related to meteorology and hydrology.
Environmental Adaptation Research Group,
Institute for Environmental Studies
Research Interests: Complexity and self-organization particularly in relation to adaptation to environmental change.
Dr. Istvan Bogardi
University of Nebraska
- Prediction of local hydrology under global climate change (National Science Foundation)
- Impact of climate change on water quality (NATO)
- Effect of global climate change on regional hydrology (National Science Foundation)
- Incorporation of fuzzy set techniques to account for uncertainties in extremes under changing climatology (National Science Foundation)
- Working together: integration engineering, the humanities and the social sciences to prevent environmental degradation (U.S. Information Agency)
- 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
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
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
Interprets satellite radiance data.
Environmental Conservation Branch
Sackville, N.B., Canada
- 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
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
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
- 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.
- 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,
- Bardossy, A. and M. Disse. 1993. Fuzzy rule-based models
for infiltration. Water Resources Research. Feb Vol. 29 (2) pp. 373-382.
- Bardossy, A., I. Bogardi and W.E. Kelly. 1989. Geostatistics
Utilizing Imprecise (Fuzzy) Information. Fuzzy Sets and Systems 31
- 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.
- Bare, B. and Mendoza, G. 1988. "A soft optimization approach
to forest land management planning." Canadian Journal of Forest Research.
- 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.
- Bare, B. and Mendoza, G. 1992. "Timber harvest scheduling
in a fuzzy decision environment." Canadian Journal of Forest Research.
- 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.
- 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.
- 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.
- Bosserman, R. and Ragade, R. 1982. "Ecosystem analysis using
fuzzy set theory." Ecological Modeling, 16, p.191-208.
- Burrough, P.A. 1989. "Fuzzy mathematical methods for soil
survey and land evaluation." J. of Soil Sci., 40, p.477-492.
- Burrough, P.A. 1989. Fuzzy mathematical methods for
soil survey and land evaluation. Journal of Soil Science, Vol. 40.
- Chameau. J. and Santamarina, J. 1989. "Knowledge-based system
for soil improvement." J. of Computing in Civil Engineering, Vol.3, No.3,
- Chang, L and Burrough, P. 1987. "Fuzzy reasoning: a
new quantitative aid for land evaluation." Soil Survey and Land Evaluation,
- 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.
- 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.
- Deshpande, A.W. , D.V. Raje and P.Khanna. 1996. Agreement
Index for Water Consumption. Eufit '96, September 2-5, 1996. pp. 1816-1820.
- Deshpande, A.W., D.V. Raje and P. Khanna. 1996. Fuzzy
Description of River Water Quality. Eufit '96, September 2-5.
- 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.
- English, M.J. 1981. The uncertainty of crop models
in irrigation optimization. Trans. of the ASAE. Nov/Dec 34(6):917-.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Leung, Y.. 1982. Approximate Characteristics of Some
Fundamental Concelpts of Spatial Analysis. Geographical Analysis,
Vol. 14, No. 1, January. pp 29-40.
- 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.
- 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.
- 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.
- McBratney, A.B. and Moore, A.W. 1985. "Application
of fuzzy sets to climatic classification." Agricultural and Forest Meteorology,
- McBratney, A.B., De Gruijter, J.J., Brus, D.J. 1992.
"Spacial prediction and mapping of continuous soil classes." Geoderma,
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Roberts, D. 1989. "Analysis of forest succession with
fuzzy graph theory." Ecological Modelling, 45, p.261-274.
- Roush, W. 1989. "Fuzzy decision analysis." Egg Industry,
- Roush, W., Bock, R., and Marszalek, M. 1989. "Evaluation
of crowding of caged laying hens using fuzzy decision analysis." Appl.
- Savchenko, C. and A. Borisov. 1997. Decision Making
Based on a Bayesian Belief Network. Eufit '97, September 8-11.
- 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.
- Schmoldt, D.L. 1990. "A simulation of plant physiological
processes using fuzzy variables." Proceedings of the 1990 National Conference
on Artificial Intelligence.
- Sher, A. and I. Amir. 1994. Optimization with Fuzzy
Constraints in Agricultural Production Planning. Agricultural Systems.
Vol. 45. pp 421-441.
- Sher, Arnold and Amir, Ilan 1994. "Optimization with fuzzy
constraints in agricultural production planning." Agricultural Systems,
- 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.
- Tang, H.J., Debaveye, J., Ruan, D., and Van Ranst, E. 1991.
"Land suitability classification based on fuzzy set theory." Pedologie,
- 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.
- Wenger, R. and Yue Rong 1987. "Two fuzzy set models for comprehensive
environmental decision-making." J. of Env. Mgt., 25, p.167-180.
- 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.
- 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.
- 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
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.
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
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?
Meteorologocal Service of Canada
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.
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
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:
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
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
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  describes comparisons between fuzzy
arithmetic and various probabilistic approaches to risk analysis.
- 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.
- 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.
- 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.
- 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].
- 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.
- 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.
- 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.
- Cooper, J.A., S. Ferson and L.R. Ginzburg. 1996. Hybrid processing of
stochastic and subjective uncertainty data. Risk Analysis 16: [in
- 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.
- Ferson, S. 1996. Fuzzy Arithmetic Demo. Applied Biomathematics, Setauket,
New York. [DOS software]
- Saila, S. and S. Ferson. 1996. Uncertainty analysis using simple fishery
[presented at international fisheries conference, Azores, June, 1996].
- Moore, D. and S. Ferson. Probabilistic exposure analysis: possible
when information is limited (as it always is). [in preparation].
Agricultural Economics, UBC
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.
this set of references is copied with permission from
- 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;
- Tang Huajun et.al., "Land Suitability Classification Based on Fuzzy Set Theory", Pedologie, XLI-3, p277-290, 1991;
- 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;
- David Roberts, "Analysis of Forest Succession with Fuzzy Graph Theory", Ecological Modelling, 45, p261-274, 1989;
- Robert Bosserman & Rammohan Ragade, "Ecosystem Analysis using Fuzzy Set Theory", Ecological Modelling, 16, p191-208, 1982;
- 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;
- P.A.Burrough, "Fuzzy Mathematical Methods for Soil Survey and Land Evaluation", J. of Soil Sci., 40, p477-492, 1989;
- D.L.Schmoldt, "An Influence Model for Qualitative Simulation of Ecological Systems", Artificial Intelligence and Growth Models for Forest Management Decisions", 1989;
- 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?);
- Bruce Bare and Guillermo Mendoza, "Fuzzy Programming Approaches in Handling Uncertainty and Imprecision in Timber Harvest Scheduling", (in above?)
- Bruce Bare and Guillermo Mendoza, "Timber Harvest Scheduling in a Fuzzy Decision Environment", Can.J.For.Res., Vol.22, 1992;
- Bruce Bare and G.Mendoza, "A Soft Optimization Approach to Forest Land Management Planning", Can.J.For.Res., Vol.18, 1988;
- A.B. McBratney and A.W. Moore, "Application of Fuzzy Sets to Climatic Classification", Agricultural and Forest Meteorology, 35, p165-185,1985;
- William Roush, "Fuzzy Decision Analysis", Egg Industry, May, 1989;
- 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.
Mark R. Fulton
Dept. of Ecology and Evolutionary Biology
Houston, TX 77005
- 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.
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
My thesis involves the use of fuzzy math and logic for ecological risk
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
Naval Research Laboratory
- AI in meteorology
- Knowledge Discovery from Databases
- Soft Computing
- Rough Set Theory
Meteorological Research Branch
Meteorological Service of Canada
Montreal, Quebec, Canada
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,
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,
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.
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.
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.
Shao, J., 2000: Fuzzy Categorization of Weather Conditions
for Thermal Mapping. Journal of Applied Meteorology: Vol.
39, No. 10, pp. 1784-1790.
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.
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.
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.
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.
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-
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.
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.
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.
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.
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.
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,
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,
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.
Foreign Faculty Member
Miyagi National College of Technology
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:
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.
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.
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
I have estimated "Green GNP" and the
"sustainability degree of Iranian economy".
I can send my dissertation
abstract for you.
RAISON AI hydrology model
Department of Mechanical Engineering
Texas A&M University
College Station, TX
We have done some work in environmental factors in engineering design using
F/L. Let me know if you need further information.
Faculty, Department of Computer Science
Bowling Green State University
Bowling Green, Ohio
"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
The latest AMS conference on Weather Analysis and Forecasting had a
session on AI so you might want to check the pre-print.
Ph.D. Research Fellow
Hydroinformatics and Knowledge Management
P.O. Box 3015
2601 DA Delft
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.
School of Computer Science
Western Washington University
Applied AI techniques to classify biota responses to stressors.
Faculty of Computer and Information Science
Trzaska 25, 1000 Ljubljana
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:
 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
 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
Please contact me if this area of application is interested for you.
Meteorologist, Environment Canada
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.
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
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
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
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
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
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
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
Chair, BISC Special Interest Group in Earth Sciences
Earth Sciences Division, MS 90-1116
Lawrence Berkeley National Laboratory
University of California at Berkeley
Berkekey Initiative In Soft Computing (BISC)
Special Interest Group in Earth Science (mirror)
- 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.
Institute for Earth Sciences
Uppsala University, Sweden
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.
95 Magnolia Ave.
Tenafly, NJ 07670
I am an ecologist who has written several image mining, image understanding software programs that employ fuzzy algorithms.
In particular, the image mining application
uses several fuzzy routines.
- Biodiversity Book
Oleg M. Pokrovsky
Main Geophysical Observatory
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
Potsdam Institute for Climate Impact Research
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
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.
Department of Environmental Science and Policy
University of California, Davis
Application of fuzzy logic to environmental problems
Application of fuzzy logic in environmental risk assessment of pollution
Neural networks and hybrid neural networks
T.Rajkumar and Hans W. Guesgen, 1996:
Fuzzy Exposure Model PP1051-1055 IEEE SMC, 1996, Beijing
T.Rajkumar and Hans. W. Guesgen, 1997:
Determination of Chemical exposure using multilayer feed forward neural networks,
PP264-267, IASTED Conference, 1997, Banff, Canada
T.Rajkumar, Hans W. Guesgen, 1997:
Analysis of chemical exposure through inhalation using hybrid neural network,
PP311-316 IEEE SMC 1997, Orlando, USA
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
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
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
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
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
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
- 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.
Department of Automatic Control and Systems Engineering
University of Sheffield, Sheffield, U.K.
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
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:
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:
- the representation and processing of imprecise data in the form
of fuzzy sets;
- the representation and processing of vague knowledge in the form
of linguistic rules with imprecise terms defined as fuzzy sets.
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.
- Fuzzy Clustering System ECO-FUCS /present application:
Fuzzy Clustering of Existing Chemicals According to their
(Friederichs et al., 1996, Ecological Modelling, v.85, No.1, pp.27-40);
- 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);
- 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).
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
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
Dr.-Ing. Matthias Schneider
sje Schneider & Jorde Ecological Engineering
70569 Stuttgart (Vaihingen)
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.
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
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
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.
Environmental Protection Agency
Research Triangle Park, NC
Epa is conducting research in fuzzy-logic-based control systems in the following areas:
Additionally, we are using neural networks to reduce transient emissions from rotary kiln incinerators. If you want more information about these projects, write me.
- control of AC induction motors for energy optimization and performance
- 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.
Research Group in WWTPs Instrumentation, Control and Diagnosis
Laboratory of Environmental Biotechnology
Avenue des Etangs
11100 Narbonne - France
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
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
Research Information Section
Hokuriku National Agricultural Experiment Station, Japan
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.
Facility for Intelligent Decision Support
Dept. of Civil Engineering
University of Manitoba
Winnipeg, Manitoba, Canada R3T 5V6
email: email@example.com or
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)
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.
Department of Geography
University of Hawaii at Manoa
2424 Maile Way, Porteus 414
Honolulu, Hawaii 96822
Tel: (808) 956-7156, Fax: (808) 956-3512
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.
Research Applications Program
National Center for Atmospheric Research
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.
- 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.
Department of Electrical Engineering
Faculty of Engineering and Technology
Jamia Millia Islamia (A Central university)
New Delhi-110025, Fax: 91-11-26982651
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.
Ph.D. Candidate on Water Resources Management
Civil Engineering Department
Sharif University of Technology
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