Overview

Computational intelligence and optimization methods transform the way we approach sustainable water management, offering tools to address complex environmental challenges. As water resources face pressure from climate change, population growth, and industrial demand, advanced algorithms enable more efficient, data-driven decision-making. These techniques support the design of smarter distribution systems, predictive maintenance, real-time monitoring, and adaptive resource planning. By integrating computational intelligence into water management, organizations can enhance resilience, reduce waste, and ensure equitable, sustainable access to clean water. Computational Intelligence and Optimization Methods for Sustainable Water Management examines water resources management principles, machine learning applications, novel approaches to wastewater treatment, and emerging technologies for water supply, conservation, and management. It explores how modern solutions can be integrated with traditional methods to achieve sustainable and effective water management. This book covers topics such as water resources, natural disasters, and machine learning, and is a useful resource for hydraulics engineers, climatologists, academicians, researchers, and environmental scientists.

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Yassine Ezaier is a dedicated researcher and educator in physical sciences, specializing in material science and fluid mechanics. He earned his PhD from Hassan II University, where he also completed his Master's and Bachelor's degrees in Physical and Chemical Sciences. Currently, Dr. Ezaier is a part-time lecturer at the Ecole Normale Supérieure de Casablanca, where he teaches courses in electronics, fluid mechanics, and solid mechanics. His research focuses primarily on water desalination, composite membranes, and sustainable water treatment methods, with numerous publications in high-impact journals. Proficient in Python and machine learning, he also mentors Master's students on material science projects. Rajae Gaamouche is a distinguished engineer and researcher specializing in electrical engineering. She earned her PhD from the École Mohammadia d'Ingénieurs in Rabat, Morocco, focusing on predictive control and modeling for hydrokinetic turbines connected to electrical grids. With extensive teaching experience as a part-time lecturer at the École Nationale Supérieure des Mines de Rabat, she has contributed significantly to renewable energy education, electronics, and power systems. Dr. Gaamouche has authored numerous publications in international journals and conferences and has collaborated on projects involving renewable energy systems, photovoltaic technologies, and marine energy. She is also proficient in advanced programming and simulation tools like MATLAB, PSPICE, and LABVIEW. Mohamed Lahby is Associate Professorat the Higher Normal School (ENS) University Hassan II of Casablanca, Morocco. His PhD in Computer Science from Faculty of Sciences and Technology of Mohammedia, University Hassan II of Casablanca, in 2013. His research interests are Green enrgy, wireless communication and network, mobility management, QoS/QoE, Internet of things, Smart cities, Optimization and Machine learning. He has published more than 35 papers (book chapters, international journals, and conferences), 1 edited book, and 1 authored book. He has served and continues to serve on executive and technical program committees of numerous international conferences such as IEEE PIMRC, ICC, NTMS, IWCMC, WINCOM, ISNCC. He also serves as a referee of manyprestigious Elsevier journals: Ad Hoc Networks, AppliedComputing and Informatics and International journal of disasterriskreduction. He organized and participated more than 40 conferences and workshops. He is the chair of many international workshops and special sessions such as MLNGSN’19, CSPSC’19, MLNGSN’20, MLNGSN’21, AI2SC ’20 and WCTCP’20, CIOT’22. He isalsoeditedmany books published in Springer and Taylor.

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