Generative AI and Optimization Techniques for Sustainable Water Management (Springer Optimization and Its Applications)

Lahby, Mohamed (EDT)/ Gaamouche, Rajae (EDT)

Springer, Berlin; Springer（2026/07発売）

• 製本 Hardcover:ハードカバー版
• 商品コード 9783032190116

Full Description

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

Contents

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-II-TOPSIS 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.