Implementation of Landslide Susceptibility Model Using Machine Learning for Semi Detailed Map Scale in Mountainous Region of Java Island

Abstract

Landslide susceptibility modeling using neural network (ANN) are applied to semi detailed volcanic-sedimentary water catchment. Annually landslide occurred in catchment area frequently in unconsolidated and weathered material combined with uncertainty in rainfall pattern that complicated landslide occurrence. Data used for analysis including landslide inventory, geology, digital elevation related data, distance to stream, and several other available data. Results show that machine learning method yield fair result data based on evaluation on Area under Curve (AUC). Thus, it can be suggested that machine learning methods for landslide susceptibility model could still be develop to produce robust prediction model with different characterization of parameter data and machine learning parameters.