Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey)

Abstract

In the current investigation, a Geographic Information System (GIS) and machine learning-based software were employed to generate and compare landslide susceptibility maps (LSMs) for the city center of Tokat, which is situated within the North Anatolian Fault Zone (NAFZ) in the Central Black Sea Region of Turkey, covering an area of approximately 2003 km2. 294 landslides were identified within the study area, with 258 (70%) randomly selected for modeling and the remaining 36 (30%) used for model validation. Three distinct methodologies were used to generate LSMs, namely Frequency Ratio (FR), Logistic Regression (LR), and Deep Learning (DL), using nine parameters, including slope, aspect, curvature, elevation, lithology, rainfall, distance to fault, distance to road, and distance to stream. The susceptibility maps produced in this study were categorized into five classes based on the level of susceptibility, ranging from very low to very high. This study used the area under receiver operating characteristic curve (AUC-ROC), overall accuracy, and precision methods to validate the results of the generated LSMs and compare and evaluate the performance. DL outperformed all validation methods compared to the others. Finally, it is concluded that the generated LSMs will assist decision-makers in mitigating the damage caused by landslides in the study area.