Fuzzy Logic and Hydrological Modeling

The hydrological sciences typically presentgrey or fuzzy information, making them quite messy and a choice challenge for fuzzy logic application. Providing readers with the first book to cover fuzzy logic modeling as it relates to water science, the author takes an approach that incorporates verbal expert views and other parameters that allow him to eschew the use of mathematics. The book’s first seven chapters expose the fuzzy logic principles, processes and design for a fruitful inference system with many hydrological examples. The last two chapters present the use of those principles in larger scale hydrological scales within the hydrological cycle.

Introduction General Fuzziness in Hydrology Why Use Fuzzy Logic in Water Sciences? References Problems Linguistic Variables and Logic General Words Linguistic Variables Scientific Sentences Fuzzy Scales Fuzzy Logic Thinking Stages Approximate Reasoning References Problems Fuzzy Sets, Membership Functions, and Operations General Crisp and Fuzzy Sets in Hydrology Formal Fuzzy Sets Membership Functions Membership Function Allocation Hedges (Adjectivized Words) Logical Operations on Fuzzy Sets References Problems Fuzzy Numbers and Arithmetics General Fuzzy Numbers Fuzzy Addition Fuzzy Subtraction Fuzzy Multiplication Fuzzy Division Extremes of Fuzzy Numbers Extension Principle References Problems Fuzzy Associations and Clusters General Crisp to Fuzzy Relationships Logical Relationships Fuzzy Logic Relations Fuzzy Compositions Logical Categorization Fuzzy Clustering Algorithms References Problems Fuzzy Logical Rules General Fuzzification “IF . . . THEN . . .” Rules Fuzzy Proposition Input Rule Base Establishment Complete Rule Base References Problems FIS General Fuzzy Inference Systems (FIS) Mamdani FIS Defuzzification Sugeno FIS Tsukamoto FIS S¸ en FIS FIS Training Triple Variable Fuzzy Systems Adaptive-Network-Based FIS (ANFIS) References Problems Fuzzy Modeling of Hydrological Cycle Elements General Simple Evaporation Modeling Infiltration Rate Model Rainfall Amount Prediction Rainfall–Runoff Relationship Rainfall–Groundwater Recharge Fuzzy Aquifer Classification Chart River Traffic Model References Fuzzy Water Resources Operation General Fuzzy Water Budget Drinking Water Consumption Prediction Fuzzy Volume Change in Reservoir Storage Crisp and Fuzzy Dynamic Programming Multiple Reservoir Operation Rule Lake Level Estimation Triple Diagrams Rule Base Logical-Conceptual Models References

Zekai Sen is a member of the Department of Civil Engineering at the Technical University of Istanbul.