This book examines the transformative potential of Generative Artificial Intelligence (GenAI) in addressing one of the most urgent global challenges: sustainable water management. It investigates how GenAI provides innovative tools for predicting water demand, optimizing resource allocation, and reducing the impacts of climate change. The book includes state-of-the-art contributions from experts in GenAI, optimization techniques, and water management, offering a comprehensive and interdisciplinary approach to the subject. The book explores key concepts, including the integration of advanced algorithms with real-world case studies, bridging the gap between cutting-edge technological innovation and practical water management strategies. The chapters delve into topics like government policy impacts, optimization techniques for pollution control, and AI-powered predictive models for aquaculture. This book is a must-read for those seeking to understand the role of GenAI in creating a smart and resilient future for water management. Essential for researchers, policymakers, and professionals in environmental science, agriculture, and sustainability, this book provides valuable knowledge and innovative approaches to addressing global water challenges.

Provides a unique convergence of generative AI and mathematical optimization techniques Integrates high-quality research on Large Language Models (LLMs), Digital Twins, and Attention-Gated Deep U-Nets Written by leading experts in the field

Autorentext

Mohamed Lahby is an associate professor at the Higher Normal School (ENS), University Hassan II of Casablanca, Morocco. He earned his Ph.D. in Computer Science from the Faculty of Sciences and Technology of Mohammedia, University Hassan II of Casablanca, in 2013. His research interests include wireless communication and networks, mobility management, QoS/QoE, the Internet of Things, smart cities, optimization, and machine learning. He has published more than 65 papers, including book chapters, international journal articles, and conference proceedings. Dr. Lahby has served and continues to serve on the executive and technical program committees of numerous international conferences, such as IEEE PIMRC, ICC, NTMS, IWCMC, WINCOM, and ISNCC. He also serves as a referee for many prestigious Elsevier journals, including Ad Hoc Networks, Applied Computing and Informatics, and the International Journal of Disaster Risk Reduction.

Dr. Rajae Gaamouche is an assistant professor at the Moroccan School of Engineering Sciences and holds a Ph.D. in Science and Engineering, specializing in Electrical Engineering, from the Mohammadia School of Engineers (EMI) in Rabat, Morocco (2021). Her research focuses on predictive control, marine current turbines, photovoltaic systems, and the optimization of renewable energy systems. She has published more than 15 papers, including book chapters, international journal articles, and conference proceedings, in reputable outlets such as the Journal of Marine Science and Application and Journal of Renewable Energy and Sustainable Development. Dr. Gaamouche has extensive teaching experience as a part-time lecturer at the National School of Mines in Rabat, where she taught courses on power electronics, MATLAB Simulink, and photovoltaic energy. She has participated in numerous international conferences and workshops and has been actively involved in organizing events such as the International Symposium on Green Technologies and Applications.

Inhalt

Impact of Government Policy on Sustainable Water Management in the Light of Generative AI in Vietnam.- Evaluating Human Expertise and Generative AI (ChatGPT) Responses to Questions on Water Challenges: A Quantitative Study.- Factors Affecting Sustainable Water Investment in the Interaction of Generative AI.- Optimization of Water Management for Reducing Health Risks in the MENA Region: A Lagrange Multiplier.- A Lab Scale Prototype: Determining the Pipeline Networks Leakage Point using Fuzzy Logic and IoT.- Hybrid Spatio-Temporal NSGA-IITOPSIS Optimization Framework for Intelligent Urban Water Network Management.- AI Enhanced Optimization of Water Management Under Climate Uncertainty.- AI-powered Generative Models for Predictive and Optimized Aquaculture Water Management.- RivUNet: An Attention-Gated Deep U-Net for Water Body Segmentation in Satellite Imagery to Enhance River Encroachment Monitoring.- Wastewater Treatment based on GenAI.- LLM and Metamodeling for Model Extraction from Smart Agriculture Requirements.- Policy-Ready Digital-Twin Framework for Hospital Water Resilience.- Optimization of Agricultural Production in the MENA Region: Under Resource Constraints and Water Stress.- Water Flow Prediction in the Black River (USA) Leveraging Evolutionary Feedforward Artificial Neural Networks and Crow Search Optimization.- Estimation and Prediction of Hydrological Variables Using Machine Learning Algorithms for groundwater management:ErfoudRadier Station in Morocco.