Reprint
Geo-Information for Watershed Processes
Edited by
September 2023
404 pages
- ISBN978-3-0365-8588-8 (Hardback)
- ISBN978-3-0365-8589-5 (PDF)
This book is a reprint of the Special Issue Geo-Information for Watershed Processes that was published in
Computer Science & Mathematics
Environmental & Earth Sciences
Summary
Discover a compelling Reprint featuring insightful scientific works across various fields centered on the significance of watersheds in shaping global landscapes. This comprehensive reprint presents cutting-edge research, offering valuable resources for researchers, practitioners, educators, and students. Delve into watershed analysis and its interdisciplinary nature, addressing pressing sustainability issues. This reprint welcomed research in fostering knowledge exchange and innovative research, making a lasting impact on scientific advancement and environmental stewardship.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
soil erosion; erosion pin; machine learning; morphometric factor; Shihmen Reservoir watershed; n/a; rill; soil erosion; neural networks; remote sensing; relationship modeling; agricultural data; geoinformation; LULC changes; modeling; observation; SWAT; Yellow River Basin; surface water area; Google Earth Engine; spatio-temporal change; influencing factors; groundwater depth; greensward land; MODFLOW; coal mining; Hailiutu River Basin (HRB); landslide evolution; landslide recovery; spatiotemporal hotspot; certainty factor analysis; landslide susceptibility prediction; semi-supervised learning; clustering by fast search and finding density peaks; random forest; extreme learning machine; water; watershed proritization; agriculture; dryland; Google Earth Engine; Mara River Basin; dry seasons; vegetation greenness; random forest regression; spatiotemporal differentiation; watershed management; watershed land surface; geo-information technology; morphology; relief; subsidence; DInSAR; Sentinel-1; groundwater extraction; aquifer behavior; changes; Google Earth Engine; sentinel; random forest; SVM; CART; Colaboratory; Amazonas region; drought monitoring; Pearl River Basin; MODIS satellite; SbAI; Google Earth Engine; coastal vulnerability index; coastal erosion; shoreline change; GIS; remote sensing; coastal watersheds; floods; compound events; flood typologies; precipitation; catchment characteristics; landslide susceptibility; machine learning; k-nearest neighbor; naïve Bayes; random forest; rainfall-induced landslides; physically based model; TRIGRS; Western Ghats; HAND; topographic wetness index; land use; hydrological landscape; topographic footprint; geometric signature