Landslide susceptibility mapping using advanced machine learning algorithms (Case study: Sarovabad city, Kurdistan province)
Baharak Motamedvaziri
1
,
Hemen Rastkhadiv
2
,
Seyed Akbar Javadi
3
,
Hasan Ahmadi
4
1 - Associate Professor, Department of Nature Engineering, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Ph.D. Student, Department of Nature Engineering, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Associate Professor, Department of Nature Engineering, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Full Professor, Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj, Iran.
Keywords: Natural hazards, Random forest algorithm, Decision tree algorithm, Kurdistan province,
Abstract :
The occurrence of landslides in mountainous areas may cause serious damage to road infrastructure, and may also lead to human deaths. Therefore, the purpose of this study is to landslide susceptibility mapping using advanced machine learning algorithms in Sarovabad city. In this study, landslide susceptibility was determined using two advanced data mining algorithms including random forest (RF) and decision tree (DT). First, the point file of 166 landslides occurred in Sarovabad city was considered as the landslide inventory map. The landslide points are divided into training data (70%) and validation data (30%). A total of 16 parameters including slope, aspect, elevation, river proximity, road proximity, river density, fault proximity, fault density, road density, precipitation, land use, NDVI, lithology, earthquake, stream power index (SPI) and topographic wetness index (TWI) were used in order to landslide susceptibility mapping. Finally, the performance of the models was evaluated using the ROC curve. The results of the ROC showed that the decision tree and random forest models have AUC values of 0.942 and 0.951, respectively. Therefore, the random forest model has the highest AUC value compared to the decision tree and was the best model for predicting the risk of landslides in the future in the study area. Landslide potential maps are efficient tools; so that they can be used for environmental management, land use planning and infrastructure development.
