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Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques

2022; Elsevier BV; Volume: 191; Linguagem: Inglês

10.1016/j.jafrearsci.2022.104535

1879-1956

Halil Akıncı,

Tree Root and Stability Studies

In this study, the performances of machine learning models, such as artificial neural networks (ANN), gradient-boosting machines (GBM), random forest (RF) and support vector machines (SVM) in rainfall-induced landslide susceptibility mapping were evaluated. For this purpose, the Arhavi, Hopa and Kemalpaşa districts of Artvin, which is one of the highest rainfall areas in Turkey, were identified as the study area. A landslide inventory comprising 533 landslide polygons (3959 pixels at 10-m resolution) was used; 70% of the pixels showing the landslides were used for training the models and the remaining 30% were used to validate the models. For landslide susceptibility modelling, 13 factors associated with landslides were considered. The area under the receiver operating characteristic curve was found to reveal the predictive capabilities of the models. As a result, the prediction rates of the ANN, SVM, RF and GBM models were found to be 93.8%, 94.8%, 96.1%, and 97%, respectively. According to the results, the GBM outperformed other models.