MULTI-HAZARD SUSCEPTIBILITY ASSESSMENT WITH HYBRID MACHINE LEARNING METHODS FOR TUT REGION (ADIYAMAN, TURKIYE)
Keywords: Landslides, Earthquakes, Floods, Random Forest, Fuzzy Inference System, Multi-hazard Susceptibility, Site Selection
Abstract. Recent Kahramanmaras earthquakes (Mw 7.7 and 7.6) occurred on 6 February 2023 have shown the importance of site selection for settlements and infrastructure considering the fact that multiple hazards may affect the same area and even interact with each other. The Kahramanmaras earthquakes triggered several landslides, which also increased the level of destruction. Here, we implemented a multi-hazard susceptibility assessment approach for Tut town of Golbasi, Adiyaman and its surroundings. Over 600 landslides were triggered in the area by the earthquakes. In addition, the region is prone to flooding and a devastating one occurred on March 15, 2023 after heavy rains. In this study, we employed co-seismic landslide inventory for landslide susceptibility assessment with random forest. Regarding flood susceptibility, a modified analytical hierarchical process was utilized based on expert opinion on factor importance. The earthquake hazard probability distribution was obtained from a distance-based interpolation of Arias intensity values. We utilized Mamdani Fuzzy Inference System for producing a multi-hazard susceptibility map from univariate maps of earthquake, landslide and flood. The result shows that the selected methods for each type of susceptibility map was suitable and the output of the study can be utilized for the site selection in Tut region, which is a crucial subject due to the need of new construction sites after the earthquakes.