An ensemble of evidence belief function (EBF) with frequency ratio (FR) using geospatial data for landslide prediction in Darjeeling Himalayan region of India

Abstract

Landslide is a natural hazards that can be extremely difficult to control without continuous monitoring. The research area is in the Himalayan Mountains region, characterized by complicated geological structures. Landslide occurs in this area and damages property, life, infrastructure, and others. So it is essential to study the susceptible areas for planning and decision-makers. Primarily, 1582 landslides were identified and divided into two distinct groups: the training dataset consisting of 1092 (70%) landslides used to develop the model and the validation dataset consisting of 490 (30%) landslides used to validate the utilized model. In this research, we have used sixteen spatial datasets, viz, topographic datasets are elevation, slope, profile curvature, topographic ruggedness index (TRI), topographic position index (TPI), topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI); hydrological datasets are rainfall and distance to drainage; geological datasets are lithology, geomorphology, distance to fault; landcover datasets are NDVI, distance to road, LULC. This study aims to perform the landslide susceptibility zonation using the performance of evidence belief function (EBF) and frequency ratio (FR) model in the Darjeeling Himalayan region. The susceptibility models have been categorized into five classes, i.e., very low, low, moderate, high, and very high, where EBF model accounting 4.51, 1.22, 25.60, 38.27, and 40.41 percent, respectively, and FR model accounting 5.02, 22.14, 31.61, 33.51, and 7.71 percent, respectively. The receiver operating characteristic and area under the curve (ROC-AUC) was used to measure the success and prediction rates for validation. The result revealed that the success rate of the EBF and FR models is 0.937 and 0.936, and the prediction rate is 0.949 and 0.953, respectively.