Abstract
Landslides have been identified as one of the costliest and deadliest natural disasters, causing tremendous damage to humans and societies. Information regarding the spatial extent of landslides is thus important to allow officials to devise successful strategies to mitigate landslide hazards. This study aims to develop a machine-learning approach for predicting landslide areas in the Tsengwen River Watershed (TRW), which is one of the most landslide-prone areas in Central Taiwan. Various spatial datasets were collected from 2009 to 2015 to derive 36 predictive variables used for landslide modeling with random forests (RF). The results of landslide prediction, compared with ground reference data, indicated an overall accuracy of 91.4% and Kappa coefficient of 0.83, respectively. The findings achieved from estimates of predictor importance also indicated to officials that the land-use/land-cover (LULC) type, distance to previous landslides, distance to roads, bank erosion, annual groundwater recharge, geological line density, aspect, and slope are the most influential factors that trigger landslides in the study region.
Highlights
Landslides are the fifth-deadliest natural disaster after windstorms, floods, earthquakes, and extreme temperature during the last 20 years [1]
Landslides are driven by gravity and are characterized by movements of solid rock, debris, and soil, causing severe human casualties, property loss, and infrastructure and environmental damage [2,3,4]
Because Taiwan is located at the subduction zone of the Philippine Sea and Eurasian Plates, collisions between these two plates have resulted in many folds and faults in the formed mountains
Summary
Landslides are the fifth-deadliest natural disaster after windstorms, floods, earthquakes, and extreme temperature during the last 20 years [1]. They occur over a wide range of spatial and temporal scales across mountainous landscapes. Landslides are severe in Taiwan and considered a major natural hazard in hilly and mountainous regions. They cause risk to life and infrastructure and are difficult to predict.
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