Fuzzy Logic in Hydrology and Water Resources

From the early application of fuzzy logic to hydrology a large amount of research has been pursued and at present, fuzzy logic has more and more become a practical tool in hydrologic analysis and water resources decision making. In this chapter the main areas of applications are highlighted. Then, one major area of hydrology, namely, hydro-climatic modeling of hydrological extremes (i.e., droughts and intensive precipitation) is selected to describe in details the methodology using fuzzy rules of inference (or in other words the fuzzy rule-based modeling technique). Results over four regions—Arizona, Nebraska, Germany and Hungary—and under three different climates—semiarid, dry and wet continental—suggest that fuzzy rule-based approach can be used successfully to predict the statistical properties of monthly precipitation and drought index from the joint forcing of macrocirculation patterns and ENSO information.

[1]  Slobodan P. Simonovic,et al.  Reservoir Systems Analysis: Closing Gap between Theory and Practice , 1992 .

[2]  Lucien Duckstein,et al.  Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems , 1995 .

[3]  Istvan Bogardi,et al.  Risk-cost optimization under uncertainty for dredged material disposal , 1999 .

[4]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[5]  I. Bogardi,et al.  Steady State Groundwater Flow Simulation With Imprecise Parameters , 1995 .

[6]  S. Moral,et al.  Learning rules for a fuzzy inference model , 1993 .

[7]  Ferenc Szidarovszky,et al.  Bayesian analysis of underground flooding , 1982 .

[8]  T. McKee,et al.  THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES , 1993 .

[9]  R. E. Carlson,et al.  Midwestern Corn Yield and Weather in Relation to Extremes of the Southern Oscillation , 1996 .

[10]  Lucien Duckstein,et al.  Multicriterion analysis of small hydropower plants under fuzzy objectives , 1986 .

[11]  J. Oh,et al.  ON SPATIAL AND TEMPORAL VARIATIONS OF THE PALMER DROUGHT SEVERITY INDEX IN KOREA , 2001 .

[12]  Istvan Bogardi,et al.  Nitrate-risk assessment using fuzzy-set approach , 1995 .

[13]  P. P. Mujumdar,et al.  A fuzzy risk approach for seasonal water quality management of a river system , 2002 .

[14]  I. Bogardi,et al.  Numerical solute transport simulation using fuzzy sets approach , 1997 .

[15]  Istvan Bogardi,et al.  Fuzzy rule-based prediction of monthly precipitation , 2001 .

[16]  Robert G. Quayle,et al.  A Historical Perspective of U.S. Climate Divisions , 1996 .

[17]  A. Timmermann,et al.  Increased El Niño frequency in a climate model forced by future greenhouse warming , 1999, Nature.

[18]  Reliability with Fuzzy Elements in Water Quantity and Quality Problems , 1991 .

[19]  Asit K. Biswas,et al.  Models for water quality management , 1981 .

[20]  S. Chib,et al.  Understanding the Metropolis-Hastings Algorithm , 1995 .

[21]  Valentina Krysanova,et al.  Assessment of nitrogen leaching from arable land in large river basins: Part II: regionalisation using fuzzy rule based modelling , 2002 .

[22]  A. Bárdossy,et al.  Automated objective classification of daily circulation patterns for precipitation and temperature downscaling based on optimized fuzzy rules , 2002 .

[23]  A. Bárdossy,et al.  Development of a fuzzy logic-based rainfall-runoff model , 2001 .

[24]  A. J. Clarke,et al.  On the Timing of Warm and Cold El Niño-Southern Oscillation Events , 1995 .

[25]  Geza Pesti,et al.  A fuzzy rule-based approach to drought assessment , 1996 .

[26]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[27]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[28]  A. Bárdossy,et al.  A hydroclimatological model of areal drought , 1994 .

[29]  Thomas C. Piechota,et al.  Drought and Regional Hydrologic Variation in the United States: Associations with the El Niño-Southern Oscillation , 1996 .

[30]  Markus Disse,et al.  Fuzzy rule-based models for infiltration , 1993 .

[31]  András Bárdossy,et al.  Conflict Analysis Using Multiple Criterion Decision Making under Uncertainty , 1996 .

[32]  F. Giorgi,et al.  Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling , 1999 .

[33]  Slobodan P. Simonovic,et al.  Modeling uncertainty in reservoir loss functions using fuzzy sets , 1999 .

[34]  Y. W. Lee,et al.  FUZZY DECISION MAKING IN GROUND WATER NITRATE RISK MANAGEMENT , 1994 .

[35]  Lucien Duckstein,et al.  Multi-objective fuzzy regression: a general framework , 2000, Comput. Oper. Res..

[36]  W. E. Kelly,et al.  Imprecise (fuzzy) information in geostatistics , 1988 .

[37]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[38]  P. B. Wright,et al.  The southern oscillation: an ocean-atmosphere feedback system? , 1985 .

[39]  Warren M. Washington,et al.  El Niño-like climate change in a model with increased atmospheric CO2 concentrations , 1996, Nature.

[40]  Lucien Duckstein,et al.  Estimation of local precipitation statistics reflecting climate change , 1993 .

[41]  Lucien Duckstein,et al.  Regional management of an aquifer for mining under fuzzy environmental objectives , 1983 .

[42]  Lucien Duckstein,et al.  Fuzzy set and probabilistic techniques for health-risk analysis , 1991 .

[43]  Wayne Woldt,et al.  Transient Ground‐Water Flow Simulation Using a Fuzzy Set Approach , 1997 .

[44]  A. Bárdossy,et al.  Fuzzy regression in hydrology , 1990 .

[45]  Lucien Duckstein,et al.  Fuzzy Rule-Based Modeling of Reservoir Operation , 1996 .

[46]  Lucien Duckstein,et al.  Fuzzy rule-based classification of atmospheric circulation patterns , 1995 .

[47]  Wayne Woldt,et al.  Fuzzy rule-based approach to describe solute transport in the unsaturated zone , 1999 .