Landslide Susceptibility Spatial Modelling Using Random Forest Algorithm: A Case Study of Malang Regency

Abstract

Landslides are disasters that cause huge losses to both human life and infrastructure. Therefore, this research purpose of carrying out landslide susceptibility spatial modelling using a random forest (RF) algorithm. This research uses 12 landslide conditioning factors to generate a landslide susceptibility map, which comprises elevation, slope, aspect, soil type, geological type, distance to river, NDVI (Normalized Different Index), river density, TWI (Topographic Wetness Index), annual rainfall, and land use. Each model was evaluated by 9 parameters including ROC (Receiver Operator Characteristic)-AUC (Area Under Curve), accuracy (acc), sensitivity (sn), specificity (sp), balanced accuracy (ba), g-mean (gm), cohen’s kappa (CK), and Matthew’s correlation coefficient (MCC). A total of 88 landslide locations were identified in Malang District using the regional disaster management authority of Malang District data. Of the 88 landslide inventories, 30% of the data were used for validation, and the remaining 70% were used for training purposes. The results show the ACC value of 0.884, 0.765 for SN, 0.962 for SP, 0.863 for GM, 0.857 for BA, 0.749 for CK, 0.876 for MCC, and 0.943 for AUC. From the entire landslide conditioning factors, the elevation parameter has the highest relative contribution level value, which is 100%. Moreover, the susceptibility map indicates that Malang District is dominated by a high susceptibility with an area of 177,208.83 ha (51% of the coverage area). 13 sub-districts that are dominated by high susceptibility levels area, including Ngantang, Kasembon, Apelgading, Pujon, Tirtoyudo, Poncokusumo, Sumbermanjing, Jabung, Dampit, Wonosari, Wagir, Dau and Gedangan sub-districts.