An ensemble approach of bi-variate statistical models with soft-computing techniques for GIS-based landslide susceptibility zonation in the Kalimpong region of Darjeeling Himalaya, India

Landslides are significant and recurring hazards in the Himalayan region, necessitating the need for landslide susceptibility zonation (LSZ) to identify landslide probable areas. This study applied three bi-variate models namely certainty factor (CF), evidential belief function (EBF), and weight of evidence (WofE), with two soft-computing models, namely artificial neural network (ANN) and random forest (RF) to predict landslides in parts of the Kalimpong region. Ensembles combining soft-computing and bi-variate models were examined and compared with traditional bi-variate models for LSZ prediction accuracy. To improve ANN and RF model performance, three non-landslide scenarios were also assessed. Nine model architectures (CF, EBF, WofE, ANN-CF, ANN-EBF, ANN-WofE, RF-CF, RF-EBF, RF-WofE) were designed to derive landslide susceptibility index (LSI) values. These LSI values were classified into five susceptible zones using the success rate curve method derived class boundaries. Model prediction performance was evaluated using the area under curve (AUC) of receiver operating characteristic curve, standard error (SE), 95% confidence interval (CI), and chi-square (χ2)-based measures. The results indicate ensemble models achieved better prediction accuracy (average AUC of 0.834) compared to bi-variate models (average AUC of 0.815), with RF-CF (AUC = 0.843, SE = 0.0092, 95% of CI = 0.825 to 0.862, and χ2 = 1478.61) and ANN-CF (AUC = 0.842, SE = 0.0093, 95% of CI = 0.824 to 0.860, and χ2 = 1435.89) models outperformed all. Additionally, the assessment found ~ 34% area is highly susceptible to landslides. It is envisaged that the present attempt will be helpful for better land use planning in the investigated area.