Fuzzy Logic and Hydrological Modeling

Fuzzy Logic and Hydrological Modeling

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Features

  • Addresses fuzzy logic as applied to hydrology and water sciences
  • Provides a straightforward hydrological design
  • Presents problem/solution methodology in water science with fuzzy inference systems based on verbal rule bases and numerical databases
  • Allows readers to solve hydrology computations without mathematics by presenting computations based on linguistic deductions
  • Includes solved examples from hydrology, hydrogeology, and water resources operation

Summary

The hydrological sciences typically present grey 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.

Table of Contents

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

Author Bio(s)

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

 
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