Landslides pose threats not only to infrastructure around the world but also to local communities. One particularly susceptible area in Taiwan is in the Zhoukou River basin, Kaoping watershed. This study aimed to produce a Classification and Regression Tree (CART) model using R software that accurately predicts landslides in this area by validating the predictions against those observed recorded landslides in this region. The landslide data were recorded in the year of 2010, a year after typhoon Morakot stroked Taiwan in 2009, triggering huge number of landslides all over the cou[ntry. This study proposed the new concept to separate landslides area into release as its source and focuses on using the topographical factors derived from Digital Elevation Model (DEM) as the independent variable in predicting landslides occurrence, including Slope, Aspect, Curvature, Topographic Wetness Index, Average Slope and Distance from the river, and an additional Geological map of the study area.
An observed landslide release occurrence layer posed as the dependent variable classifier in the model. First, data sampling strategies applied show an optimal model to be created with the highest Area Under Curve (AUC) value of 0.73. Next, this model identified the most influential factors causing landslides by using information gain’ statistics in R software. Aspect, were determined as being most influential factor, where Distance from river, and Slope as second and third most influential.
The concept of release area separation showed a better AUC value model compared to the model using conventional full landslide inventory. The decision tree model also showed a reliable result when compared to logistic regression and random forest using the same data sampling, with the AUC value of 0.73, 0.65, and 0.81 respectively. The results have proven that decision tree model is suitable for producing landslide susceptibility map.

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