This book summarizes the latest research and developments related to the application of nature-inspired metaheuristic algorithms coupled with artificial neural networks (ANNs) in hydrology. The book covers the theoretical foundations, models and methods, structure, frameworks and analysis of applying novel ANNs in hydrology. It starts with the introduction of ANNs as a black box model, followed by the coupling of various metaheuristic algorithms with ANNs to form novel neural network models for solving real-world problems in hydrology, including Particle Swarm Optimization (PSO) for rainfall-runoff modeling, Bat Optimization (Bat) and Cuckoo Search Optimization (CSO) for future rainfall prediction, the Whale Optimization Algorithm (WOA) and Salp Swarm Optimization (SSO) for future water level prediction, Grey Wolf Optimization (GWO), Multi-Verse Optimization (MVO), the Sine Cosine Algorithm (SCA) and the Hybrid Sine Cosine and Fitness Dependent Optimizer (SC-FDO) for imputing missing rainfall data.
Associate Professor Kuok King Kuok earned his PhD from Universiti Teknologi Malaysia in 2010. Dr Kuok has contributed significantly to the scholarly landscape, authoring over 130 articles. Dr Kuok's diverse research portfolio spans critical areas, including flood forecasting, imputing missing data, filter membrane development, climate change impacts, low-impact development, sustainable water supply, building information modeling, and project management. Md Rezaur Rahman is Senior Lecturer and Associate Professor in the Department of Chemical Engineering and Energy Sustainability, Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS), Malaysia. He received his PhD from the University Malaysia Sarawak, Malaysia. He has more than 12 years of experience in teaching, research, and working with industry. So far, he has published 8 books and 20 book chapters, and more than 167 international journal papers. He is also listed as a World's Top 2% Scientist by Stanford University, USA.
