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"Landslide Susceptibility Mapping in a Mountainous Area Using Machine L" by Himan Shahabi, Reza Ahmadi et al.

Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locatio ....

Decision Tree , Amyaran Sarvabad Road , Machine Learning , Random Forest , Support Vector Machine ,

SDSC's e-Decision Tree Still Serving as Essential Tool for Schools Navigating COVID

SDSC's e-Decision Tree Still Serving as Essential Tool for Schools Navigating COVID
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