Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya
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- Omid Rahmati & Ali Haghizadeh & Hamid Reza Pourghasemi & Farhad Noormohamadi, 2016. "Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1231-1258, June.
- Hamed Ahmadpour & Ommolbanin Bazrafshan & Elham Rafiei-Sardooi & Hossein Zamani & Thomas Panagopoulos, 2021. "Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection," Sustainability, MDPI, vol. 13(18), pages 1-24, September.
- Mareike Ließ & Johannes Schmidt & Bruno Glaser, 2016. "Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-22, April.
- Massimo Conforti & Pietro Aucelli & Gaetano Robustelli & Fabio Scarciglia, 2011. "Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 56(3), pages 881-898, March.
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- Jorge da Paixão Marques Filho & Antônio José Teixeira Guerra & Carla Bernadete Madureira Cruz & Maria do Carmo Oliveira Jorge & Colin A. Booth, 2024. "Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba Do Sul Middle Valley, Southeast Brazil," Land, MDPI, vol. 13(10), pages 1-21, October.
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Keywords
soil erosion; land degradation; sustainable land management; landscape restoration; spatial prediction; machine learning;All these keywords.
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