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Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia

Aditian, Aril, Kubota, Tetsuya, Shinohara, Yoshinori
Geomorphology 2018 v.318 pp. 101-111
geographic information systems, graphs, hills, landslides, neural networks, prediction, rain, regression analysis, streams, Indonesia
This study aims to evaluate landslide causative factors in landslide susceptibility assessments and to compare landslide susceptibility models based on the bivariate frequency ratio (FR), multivariate logistic regression (LR), and artificial neural network (ANN). The majority of landslide occurrences in Ambon, Indonesia is induced by heavy rainfall events where slope failures occur mostly on steep slopes thereby endangering municipality areas at the base of the hills. Eight landslide causative factors were considered in the landslide susceptibility assessments. The causative factors were elevation, slope angle, slope aspect, proximity to stream network, lithology, density of geological boundaries, proximity to faults, and proximity to the road network. The output susceptibility maps were reclassified into five classes ranging from very low to very high susceptibility using Jenks natural breaks method. Twenty percent of all mapped landslides were used as the validation of the susceptibility models. The validity and the accuracy of each model were tested by calculating areas under receiver operating characteristic curves (ROCs), and the areas under the curve (AUC) for the success rate curves of FR, LR, and ANN were 0.688, 0.687, and 0.734, respectively. The AUC for the prediction rate curve of FR, LR, and ANN were 0.668, 0.667, and 0.717, respectively. All findings of the models show good results with the accuracy of all models being higher than 66%. The ANN method proved to be superior in explaining the relationship of landslide with each factor studied.