Computational Intelligence for Water and Environmental Sciences

This book provides a comprehensive yet fresh perspective for the cutting-edge CI-oriented approaches in water resources planning and management. The book takes a deep dive into topics like meta-heuristic evolutionary optimization algorithms (e.g., GA, PSA, etc.), data mining techniques (e.g., SVM, ANN, etc.), probabilistic and Bayesian-oriented frameworks, fuzzy logic, AI, deep learning, and expert systems. These approaches provide a practical approach to understand and resolve complicated and intertwined real-world problems that often imposed serious challenges to traditional deterministic precise frameworks. The topic caters to postgraduate students and senior researchers who are interested in computational intelligence approach to issues stemming from water and environmental sciences.

Reviews comprehensively CI-oriented topics that address the needs of general and specialist readers Contains practical examples that provide a detailed and practical concept of each topic to the readers Provides in-depth definitions and concepts of each topic that will improve readers' attitudes toward future

Autorentext
Dr. Omid Bozorg-Haddad is a distinguished professor at the University of Tehran, Iran. His teaching and research interests include water resources, energy, and environmental systems analysis, engineering, planning, and management as well as application of simulation techniques and optimization algorithms in water-related systems. He has published more than 40 books and chapters, 300 journal, and 200 conference papers.

Klappentext

This book provides a comprehensive yet fresh perspective for the cutting-edge CI-oriented approaches in water resources planning and management. The book takes a deep dive into topics like meta-heuristic evolutionary optimization algorithms (e.g., GA, PSA, etc.), data mining techniques (e.g., SVM, ANN, etc.), probabilistic and Bayesian-oriented frameworks, fuzzy logic, AI, deep learning, and expert systems. These approaches provide a practical approach to understand and resolve complicated and intertwined real-world problems that often imposed serious challenges to traditional deterministic precise frameworks. The topic caters to postgraduate students and senior researchers who are interested in computational intelligence approach to issues stemming from water and environmental sciences.

Inhalt
Optimization Algorithms.- Data mining.- Machine Learning.- Artificial Intelligence.- Deep Learning.- Expert Systems.- Probabilistic Models.- Bayesian models.- Fuzzy logic and Fuzzy Theory.