Spatial prediction of landslides along National Highway-6, Hoa Binh province, Vietnam using novel hybrid models

Abstract

Landslides are considered to be a significant risk to life and property all over the world in general and in Vietnam in particular. Spatial prediction of landslides is required to reduce the landslides risk and to plan the development of hilly areas. In this regard, the accurate landslide susceptibility maps are very useful tool for decision-makers to identify areas where new landslides are likely to occur for planning timely adequate remedial measures. For the development of landslide susceptibility maps, seven hybrid models were developed namely AdaBoost-LMT (ABLMT), bagging-LMT (BLMT), cascade generalization-LMT (CGLMT), dagging-LMT (DLMT), MultiBoostAB-LMT (MBLMT), rotation forest-LMT (RFLMT) and random sub-space-LMT (RSSLMT) with logistic model trees (LMTs) as a base classifier. The model’s performance and validation were assessed through various statistical indices, such as sensitivity (SST), specificity (SPF), accuracy (ACC), area under ROC curve (AUC), RMSE and k index. The results show that all these models are performing well for the prediction of landslide susceptibility in the study area, but the performance of the RSSLMT model is the best (AUC: 0.816). In this study, open-source data has been used for the development of landslide susceptibility maps Along National Highway-6, passing through Hoa Binh province, Vietnam. These approaches can be applied also in other hilly regions of the world which are susceptible to landslides for better landslides prevention and management.