Spatial assessment of landslide susceptibility mapping generated by fuzzy-AHP and decision tree approaches

The current study aimed to elaborate a landslide susceptibility map (LSM) in the region that has high landslide risk and is located within the borders of the Atakum district of Samsun province, Turkey. For this aim, topographic, geological, land use, and soil indica-tors were considered in terms of landslide-conditioning and landslide-triggering parameters and they were weighted through the Fuzzy -Analytic Hierarchy Process (Fuzzy-AHP) approach. Then landslide susceptibility maps were generated at 4 different class levels (Very low-H1, Low-H2, Moderate-H3, High-H4) by using a weighted linear combination technique integrated with the Geographic Informa-tion System (GIS). In addition, it was investigated the predictability of susceptibility maps by using a decision tree algorithm named CHAID (Chi-Square Automatic Interaction Detection). According to the results, the 'very low' and 'low' susceptibility class, corre-sponding to 29.8 % of the total area in the susceptibility map, was estimated with 100 % accuracy through the decision tree algorithm. 70.2 % of the total area, specified in 'medium' (H3-68.6 %) and 'high' (H4-1.6 %) susceptibility classes in the map created Fuzzy-AHP, was found to be in the 'medium' susceptibility class, as a result of the estimation made with the decision tree. Although the H1, H2, and H3 classes were successfully estimated (p < 0.05) by using the weights obtained through the Fuzzy-AHP approach with the help of the decision tree algorithm, the estimation accuracy of the H4 class was 'low' (AUC [Area Under Curve]: 0.773; p > 0.05), at the end of the research.(c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

Automated summary

This study developed a landslide susceptibility map in the high-risk region of Atakum district, Samsun province, Turkey. The map was created using topographic, geological, land use, and soil indicators weighted by the Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) approach, and integrated with the Geographic Information System (GIS). The predictability of the susceptibility map was also assessed using the decision tree algorithm CHAID. The results showed high accuracy for the 'very low' and 'low' susceptibility classes, but lower accuracy for the 'high' class.