Computational Intelligence for Water and Environmental Sciences

This book provides a comprehensive perspective on cutting-edge CI-oriented approaches in water resources planning and management, covering meta-heuristic evolutionary optimization algorithms, data mining techniques, probabilistic and Bayesian-oriented frameworks, fuzzy logic, AI, deep learning, and expert systems. It is designed for postgraduate students and senior researchers interested in computational intelligence approaches to water and environmental sciences.

Format: Hardback
Length: 540 pages
Publication date: 09 July 2022
Publisher: Springer Verlag, Singapore

This comprehensive yet innovative book offers a fresh perspective on cutting-edge CI-oriented approaches in water resources planning and management. It delves deep into various topics, including meta-heuristic evolutionary optimization algorithms (such as GA, PSA, and more), data mining techniques (such as SVM, ANN, and more), probabilistic and Bayesian-oriented frameworks, fuzzy logic, artificial intelligence (AI), deep learning, and expert systems. These approaches provide a practical means to comprehend and address complex and interconnected real-world problems that often pose significant challenges to traditional deterministic precise frameworks. The book is particularly relevant to postgraduate students and senior researchers interested in leveraging computational intelligence to address issues related to water and environmental sciences.

Introduction:
The field of water resources planning and management faces numerous challenges, including the need to optimize water allocation, manage water quality, and address climate change impacts. Traditional approaches to these problems often rely on deterministic models and precise calculations, which can be time-consuming and limited in their ability to handle complex and uncertain situations.

Computational Intelligence Approach:
In recent years, computational intelligence (CI) has emerged as a powerful tool for addressing these challenges. CI encompasses a wide range of techniques, including optimization algorithms, data mining, probabilistic modeling, and artificial intelligence, which can help planners and managers make more informed decisions and develop more effective strategies.

Chapter 1:
The first chapter of the book provides an overview of CI-oriented approaches in water resources planning and management. It discusses the importance of integrating CI into water resources decision-making processes and highlights the benefits of using these approaches. The chapter also introduces the key concepts and techniques used in CI, such as optimization algorithms, data mining, and probabilistic modeling.

Chapter 2:
In the second chapter, the book delves into meta-heuristic evolutionary optimization algorithms. It discusses the use of these algorithms in water resources planning, including their advantages and disadvantages. The chapter also provides examples of how these algorithms have been applied in real-world cases, such as water allocation and reservoir management.

Chapter 3:
The third chapter focuses on data mining techniques. It discusses the use of data mining in identifying patterns and trends in water resources data, which can help planners and managers make more informed decisions. The chapter also provides examples of how data mining has been used in water quality monitoring, water demand forecasting, and climate change impact assessment.

Chapter 4:
The fourth chapter explores probabilistic and Bayesian-oriented frameworks. It discusses the use of these frameworks in water resources planning, including their advantages and disadvantages. The chapter also provides examples of how these frameworks have been applied in real-world cases, such as flood risk assessment and water quality modeling.

Chapter 5:
The fifth chapter explores fuzzy logic. It discusses the use of fuzzy logic in water resources planning, including its advantages and disadvantages. The chapter also provides examples of how fuzzy logic has been used in water quality monitoring, water demand forecasting, and climate change impact assessment.

Chapter 6:
The sixth chapter focuses on artificial intelligence. It discusses the use of AI in water resources planning, including its advantages and disadvantages. The chapter also provides examples of how AI has been used in water quality monitoring, water demand forecasting, and climate change impact assessment.

Chapter 7:
The seventh chapter explores deep learning. It discusses the use of deep learning in water resources planning, including its advantages and disadvantages. The chapter also provides examples of how deep learning has been used in water quality monitoring, water demand forecasting, and climate change impact assessment.

Chapter 8:
The eighth chapter explores expert systems. It discusses the use of expert systems in water resources planning, including their advantages and disadvantages. The chapter also provides examples of how expert systems have been used in real-world cases, such as water quality monitoring and decision support systems.

Conclusion:
In conclusion, this book provides a comprehensive yet innovative perspective on cutting-edge CI-oriented approaches in water resources planning and management. It offers a practical approach to understanding and resolving complicated and intertwined real-world problems that often impose serious challenges to traditional deterministic precise frameworks. The book is particularly relevant to postgraduate students and senior researchers interested in leveraging computational intelligence to address issues related to water and environmental sciences.

Weight: 1004g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811925184
Edition number: 1st ed. 2022