Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis

Abstract

Naqadeh Region (NR) is one of the most sensitive regions regarding geo-hazards ‎occurrence in Northwest of Iran. The landslides triggering parameters that ‎identified for the studied region are classified as elevation, aspect, slope angle, ‎lithology, drainage density, distance to river, weathering, land-cover, ‎precipitation, vegetation, distance to faults, distance to roads, and distance to ‎the cities. These triggering factors are selected based on conducting field ‎survey, remote-sensing investigation, and historical development background ‎assessment. Regarding the investigations, 12 large-scale, 15 medium-scale, and 30 small-scale historical landslides ‎(57 in total) were recorded in the NR. The historical landslides were used to provide ‎sensitive area with high probability of ground movements. The objectives of this study are multifaceted, aiming to address critical gaps in understanding and predicting landslide susceptibility in the NR. First, the study seeks to evaluate and compare the effectiveness of ‎support-vector machine (SVM), multilayer perceptron (MLP), and decision tree ‎‎(DT) algorithms in predicting landslide susceptibility. So, as methodology, the ‎presented study used comparative models for landslide susceptibility based on ‎SVM, MLP, and DT approaches. The predictive models were compared based on model ‎accuracy as the area under the curve of the receiver operating characteristic ‎curve. According to the estimated results, MLP is the highest rank of overall ‎accuracy to provide susceptibility maps for landslides in NR. From a perspective of ‎the risk ability, the west and south-west sides of the county were identified within ‎the hazard area.