Springer Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation: Theory and Practice of Hazard Mitigation (Springer Transactions in Civil and Environmental Engineering)

Product Description This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables. Predictive modelling is a consolidated discipline used to forewarn the possibility of natural hazards. In this book, experts from numerical weather forecast, meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making contribute towards an interdisciplinary framework to construct potent models for hazard risk mitigation. The book will help advance the state of knowledge of artificial intelligence in decision systems to aid disaster management and policy-making. This book can be a useful reference for graduate student, academics, practicing scientists and professionals of disaster management, artificial intelligence, and  environmental sciences.  From the Back Cover This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables. Predictive modelling is a consolidated discipline used to forewarn the possibility of natural hazards. In this book, experts from numerical weather forecast, meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making contribute towards an interdisciplinary framework to construct potent models for hazard risk mitigation. The book will help advance the state of knowledge of artificial intelligence in decision systems to aid disaster management and policy-making. This book can be a useful reference for graduate student, academics, practicing scientists and professionals of disaster management, artificial intelligence, and  environmental sciences.  About the Author Professor Ravinesh Deo is currently an Associate Professor, and Associate Editor for Stochastic Environmental Research & Risk Assessment, ASCE Journal Hydrologic Engineering journal (USA), Director for Postgraduate Data Science Programs and a Research Leader in Artificial Intelligence at the University of Southern Queensland, Australia. As Applied Scientist with proven leadership in artificial intelligence, his research aims to develop decision-systems with machine learning, heuristic and metaheuristic algorithms to improve real-life predictive systems, especially using deep learning, convolutional neural network and long- short-term memory network. He was awarded many 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 the Endeavour Executive Fellowship. He is a member of numerous scientific bodies and has won multiple Publication Excellence Awards in both School and University Categories, Head of Department Research Award, Dean’s Commendation for Postgraduate Research Supervision, BSc Gold Medal for Academic Excellence and he was also the Dux of Fiji in Year 13 examinations. Professor Deo held visiting positions at the United Stations Tropical Research Institute (Panama), Chinese Academy of Sciences, Peking University, Northwest Normal University, University of Tokyo, Kyoto and Kyushu University, University of Alcala Spain, McGill University and National University of Singapore. He has undertaken knowledge exchange research programs in Singapore, Japan, Europe, China, USA and Canada and also secured international standing by researching innovative problems with global researchers. He has published several Books with Springer Nature, Elsevier and IGI Global and achieved more than 185 publications, more than 140 Q1 journals, including refereed conferences, Edited Books and book chapters. Professor Deo’s research papers have been cited over 4,000 times that provides a Google Scholar H-Index as 36 and a Field