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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 asp… Read more
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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.
Geologists, Hydrogeologists, Hydrologists, Hydraulic engineers, Geomorphologists, Geographers, and Disaster management professionals, Decision makers, Land-use planners, Policy makers, Data scientist, Machine learning experts, GIS Specialists, Professors, Researchers, and Students belonging to geology, geography, geoinformatics, hydrology, hydroinformatics, and machine learning fields.
I. Surface water resources management
1. Glacier monitoring
2. Precipitation nowcasting
3. Daily runoff forecasting
4. Stream flow forecasting
5. Identification of marine debris
6. Prediction of harmful algal blooms
7. Prediction of surface water quality
8. Identification of wetlands and the change detection
9. Daily reservoir inflow forecasting
10. Prediction of lake water temperature
11. Change detection of surface water bodies
12. Estimation of snow cover extent and depth
13. Forecasting of water level in lakes
14. Identification of saltwater intrusion
II. Groundwater resources management
15. Groundwater potential zone mapping
16. Identification of groundwater contaminant source
17. Groundwater quality predictions
18. Groundwater level modelling
19. Groundwater vulnerability mapping
20. Identification of artificial groundwater recharge zones
III. Hydro-climatological disaster management
21. Glacier lake outburst flood mapping
22. Flood susceptibility/hazard/risk zone mapping
23. Prediction of flash flood susceptibility
24. Estimation of flood damage
25. Assessment of drought risk or vulnerability
26. Forecasting of drought for dryland region
27. Identifying disaster-related tweets and their spatial and temporal context
28. Prediction of sea level changes
29. Coastal flood modelling
30. Snow avalanche hazard prediction
31. Rainfall induced landslide modelling
32. Mapping of shoreline changes
33. Identification of waterlogged areas
34. Flood inundation mapping
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