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

Machine Learning for the Management of Water Resources and Hydro-Climatological Disasters provides a comprehensive description and application of ML and DL methods in all major aspects of water resources management and hydro-climatological disasters. Sections cover surface water resources management, groundwater resources management, and hydro-climatological disaster management. Chapters address the major hydro-climatological disasters which pose a threat to communities, include flooding, drought or water scarcity, rainfall induced landslides, snow avalanches, sea level rise and coastal erosion, along with the demarcation of hazard or susceptible zones, inundation zones, assessments of damage, and the management of these disasters.

This book enables researchers and students to gain insights on machine learning (ML) and deep learning (DL) methods and applications. This 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.

• Addresses all hydro-climatological hazards (flooding, rainfall-induced landslides, drought, coastal erosion, shoreline changes, etc.)
• Summarizes the latest and most advanced models, such as machine learning models and deep learning models
• Addresses the management of both surface water and groundwater