Abujayyab, Sohaib K MKaras, İsmail Rakip2024-09-292024-09-292020https://search.trdizin.gov.tr/tr/yayin/detay/496049https://hdl.handle.net/20.500.14619/11545Landslides represent a continuous hazard for population and infrastructure. Mapping the landslide susceptibility is an essential issue to avoid the landslides risks. The aim of this paper is to produce a high-accuracy model for landslide susceptibility mapping in Refahiye district in Turkey. The model employed shallow neural networks for landslide susceptibility mapping, while bivariate spearman correlation test was utilized to select the related factors to extract the appropriate data and reduce the computation time of training and mapping. 12 out of 21 spatial factors were selected as relevant factors using Spearman correlation test. Relevant factors are geology, distance from roads, distance from geological faults, distance from water streams, flow direction, aspect, hillshade, heat load index, slope/aspect transformation, site exposure index, compound topographic index, and elevation. The generated dataset was divided into training, validation, and testing datasets using 10-folds cross-validation method. The TrainIm was found to be the best training function with an overall accuracy of 86.3%. The developed NN model was tested using IRIS benchmark dataset and showed higher performance against the logistic regression algorithm. As a result, shallow neural networks method was successfully applied in landslide susceptibility mapping in this study and the method is recommended for future studies.eninfo:eu-repo/semantics/openAccessArticle772614960491