Integrated assessment of landslide susceptibility in the Kalaleh Basin, Golestan Province, Iran using novel SVR-GOA ensemble validated with BRT, ANN, and elastic net models

Abstract

Abstract Landslides cause severe environmental problems, such as severe damages to infrastructures (i.e., bridges, roads, network masts, and buildings) and agricultural lands, across many parts of the world. Unfortunately, a high degree of accuracy in landslide mapping and prediction is still challenging due to the complicated interactions of many factors. The goal of this study was to create and introduce a new ensemble model (support vector regression–grasshopper optimization algorithm (SVR–GOA)) validated alongside artificial neural network (ANN), boosted regression tree (BRT), and elastic net models for landslide susceptibility modeling taking the Kalaleh Basin in Iran as a case study. For this objective, a total of 140 landslides were considered and 16 conditioning factors used to construct a geographic database. Subsequently, variance inflation factor and tolerance indices were used to test the multicollinearity of the hazard conditioning variables. The relative significance of these factors and their connections to the locations of the landslides were determined using random forest. The development and validation of the four landslide models, SVR-GOA, ANN, BRT, and elastic net, were then performed. This study is the first to implement the SVR-GOA in landslide mapping. The effectiveness of the models was evaluated using area under the curve (AUC), kappa, and root mean squared error values. The outcome indicated that the lithology, slope degree, rainfall, topography position index, topography wetness index, surface area, and landuse/landcover were the most influential conditioning factors. All of the models provided predictions with good degree of fit, with the SVR-GOA performing better than others. The models performed in the order, at validation phase: SVR-GOA (AUC = 0.930) > ANN (AUC = 0.833) > BRT (AUC = 0.822) > elastic net (AUC = 0.726). Our novel approach employing the SVR-GOA ensemble in landslide mapping would help advance landslide research on regional, national, and global scales.