Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region

Abstract

The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e. multilayer perception neural nets (MLP), kernel logistic regression (KLR), random forest (RF), and multivariate adaptive regression splines (MARS); novel ensemble approaches i.e. MLP-Bagging, KLR-Bagging, RF-Bagging and MARS-Bagging in the Kurseong-Himalayan region. For the ensemble models the RF, KLR, MLP and MARS were used as base classifiers, and Bagging was used as meta classifier. Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide. Compiling 303 landslide locations to calibrate and test the models, an inventory map was created. Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility. Applying receiver operating characteristic (ROC), precision, accuracy, incorrectly categorized proportion, mean-absolute-error (MAE), and root-mean-square-error (RMSE), the LSMs were subsequently verified. The different validation results showed RF-Bagging (AUC training 88.69% & testing 92.28%) with ensemble Meta classifier gives better performance than the MLP, KLR, RF, MARS, MLP-Bagging, KLR-Bagging, and MARS-Bagging based LSMs. RF model showed that the slope, altitude, rainfall, and geomorphology played the most vital role in landslide occurrence comparing the other LCFs. These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.