Machine Learning for the Management of Water Resources and Hydro-Climatological Disasters

Machine Learning for the Management of Water Resources and Hydro-climatological Disasters is divided into three sections: surface water resources management, groundwater resources management, and hydro-climatological disaster management. The rapid increase in the population, unscientific development practices, overexploitation, waste discharge from households and industries, and other factors are endangering surface and groundwater resources. The main threats to water resources are contamination, depletion of water levels and quality, sea water intrusion, growth of algal blooms, etc. The first two sections of this book address the majority of these problems. The major hydro-climatological disasters which pose a threat to communities include flooding (riverine floods, flash floods, coastal floods, glacial lake outburst floods, etc.), drought or water scarcity, rainfall induced landslides, snow avalanches, sea level rise and coastal erosion. The demarcation of hazard or susceptible zones, inundation zones, and assessment of damage are equally important in the effective management of disasters. The third section of this book covers most of these disasters and its management.This book enables researchers and students to get an insight on the machine learning (ML) and deep learning (DL) methods, and its applications. A comprehensive description and application of ML and DL methods to all the major aspects of water resources management and hydro-climatological disasters makes this book more relevant to the research community. The book should be of great interest to geologists, geomorphologists, hydrologists, geographers, researchers, students as well as disaster management professionals focusing on the management of water resources and hydro-climatological disasters.