Landslide Susceptibility Assessment and Management Using Advanced Hybrid Machine Learning Algorithms in Darjeeling Himalaya, India

Abstract

AbstractThe present study evaluated the landslide (LS) susceptibility using RBF net, Naïve-Bayes Tree, Random subspace, and Rotational forest advanced hybrid machine learning (HML) algorithm in landslide hazard-prone area of Kurseong, Darjeeling Himalaya, India. The locations of landslides were detected by field surveys. 352 LS coordinates have been derived, displayed as an LS inventory map to calibrate LS susceptibility models, and used to authenticate the models. 16 LCFs (landslide conditioning factors) were utilized to prepare LS susceptibility maps. The developed landslide models were validated using two statistical methods, i.e., the mean absolute error (MSE) and the root mean square error (RMSE) as well as the receiver operating characteristics (ROC), efficiency, and accuracy. The results of the accuracy measures (area under curve for RBF net = 85.76%, NB tree = 86.54%, Random sub = 87.29%; and Rotational for = 84.81%) revealed that all models have good potentiality to forecast the landslide susceptibility in the Kurseong region of Darjeeling Himalaya. The Random subset model achieved higher accuracy (ROC = 87.29%; MSE = 0.145; and RMSE = 0.118) than other used models. The research revealed that in the fellow land, plantation areas, sides of highways, and structural hills where elevation is above 1150 m and slope ranges from 26° to 69° the susceptibility to landslide is very high. The prepared landslide susceptibility maps can be helpful in introducing location-specific proper management strategies for reducing landslide hazards in Kurseong region of Darjeeling Himalaya.KeywordsHybrid machine learning modelsRBF netNaïve-Bayes TreeRandom subspaceRotational forestKurseong