Modeling Landslide Hazard Using Machine Learning: A Case Study of Bogor, Indonesia

Abstract

Landslides occur in many parts of the world. Well-known drivers, such as geological activities, are often enhanced by violent precipitation in tropical regions, creating complex multi-hazard phenomena that complicate mitigation strategies. This research investigated the utility of spatial data, especially the digital elevation model of SRTM and Landsat 8 remotely sensed data, for the estimation of landslide distribution using a machine learning approach. Bogor Regency was chosen to demonstrate the approach considering its vast hilly/mountainous terrain and high rainfall. This study aimed to model landslide hazards in Sukajaya District using random forests and analyze the key variables contributing to the isolation of highly probable landslides. The initial model, using the default settings of random forest, demonstrated a notable accuracy of 93%, with an accuracy ranging from 91 to 94%. The three main predictors of landslides are rainfall, elevation, and slope inclination. Landslides were found to occur primarily in areas with high rainfall (2,668–3,228 mm),elevations of 500 to 1,500 m, and steep slopes (25–45%). Approximately 4,536 ha were potentially prone to landslides, while the remaining area (> 12,000 ha) appeared relatively sound.