Producing Landslide Susceptibility Maps Using Statistics and Machine Learning Techniques: The Rize-Taslidere Basin Example

Abstract

As a disaster type, landslides cause significant life and economic losses; hence, producing landslide susceptibility maps is a priority research topic. This study aims to perform a landslide susceptibility analysis for shallow landslides by using statistics and machine learning techniques and evaluate the model performance using the Rize-Talidere Basin as an example. First, literature was examined. Next, a detailed research was performed on the study area characteristics and the landslide inventory creation. Fifteen parameters (i.e., land use, lithology, elevation, slope, aspect, roughness, plan curvature, profile curvature, stream erosion index, topographic humidity index, sediment-carrying capacity, drainage density, distance to drainage, road density, and distance to road) produced by the geographic information system techniques were used as the input parameters in producing the landslide susceptibility map. Using the landslide inventory and input parameters, a parameter analysis was performed for the landslide susceptibility map in five classes by employing the frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) methods. The area under the curve and the area under the relative operating curve (AUC) were used to evaluate the model performance. The results show FR of 0.72, LR of 0.83, and ANN of 0.87. Although the ANN technique provided results with a higher accuracy, the LR technique that was near accurate was usable.