Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, preventing landslides occurrences to minimize losses. variety of techniques employed susceptibility; however, their capability differs depending on the studies. The aim research produce Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). inventory contained total 140 locations which were randomly separated into training testing with ratio 70:30. Nine conditioning factors selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance road, river, lithology, rainfall. area under curve (AUC) several statistical measures analyses (sensitivity, specificity, accuracy, positive predictive value, negative value) used validate model. ANN was considered achieved very good results validation assessment, AUC value 0.940 both datasets. This study found rainfall be most crucial factor affecting occurrence Basin, 0.248 weight index, followed by road (0.200) elevation (0.136). showed susceptible located north-east Basin. might useful development planning management prevent