Elsevier Water Engineering Modeling and Mathematic Tools

Product Description Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering. This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering. Review A comprehensive reference presenting different techniques and models for water engineering, including their practical applications and case studies About the Author Professor Pijush Samui is Associate Professor at National Institute of Technology, Patna, India. He is an established researcher in the application of Artificial Intelligence (AI) for solving different problems in engineering. Samui has published journal articles, peer reviewed conference papers, book chapters and 4 books. He is also holding the position of Visiting Professor at Far East Federal University (Russia). Hossein Bonakdari, Ph.D, P.Eng., earned his Ph.D in Civil Engineering at the University of Caen-France. He has worked for several organizations like most recently as faculty member of department of Soil and Agri-Food Engineering at Laval University, Quebec, Canada. His fields of specialization include: water resources management; hydrological modelling; artificial intelligence; sustainable development; time series. Results obtained from his researches have been published in more than 250 published papers in international journals (h-index=35). He has more than 150 presentations in national and international conference and has published two books. He has secured several external research funding sponsored by government and related water resources companies/industries. He is currently leading several research projects in collaboration with industrials partners. He is ranked among the top 2.5% of the world's top researchers on ResearchGate. In 2020, Dr Bonakdari classified in the list of 2% of the most influential people of science in the world. Professor Ravinesh Deo is an Associate Professor at University of Southern Queensland, Australia, Program Director for Postgraduate Science Program and Research Leader in Artificial Intelligence. He also serves as Associate Editor for two international journals: Stochastic Environmental Research and Risk Assessment and the ASCE Journal Hydrologic Engineering journal (USA). As an Applied Data Scientist with proven leadership in artificial intelligence, his research develops decision-systems with machine learning, heuristic and metaheuristic algorithms to improve real-life predictive systems especially using deep learning explainable AI, convolutional neural networks and long short-term memory networks. He was awarded internationally competitive fellowships including Queensland Government U.S. Smithsonian Fellowship, Australia-India Strategic Fellowship, Australia-China Young Scientist Exchange Award, Japan Society for Promotion of Science Fellowship, Chinese Academy of Science Presidential International Fellowship and Endeavour Fellowship. He is a member of scientific bodies, won Publication Excellence Awards, Head of Department Research Award, Dean’s Commendation for Postgraduate Supervision, BSc Gold Medal for Academic Excellence and he was the Dux of Fiji in Year 13 examinations. Profe