Comparison of Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon's Entropy Models in Erosion Susceptibility Analysis in Bingöl (Türkiye) with GIS

Soil erosion is one of the most important and critical processes occurring in Türkiye, as in all parts of the world. It is of great importance to understand the processes that occur as soil erosion continues. The aim of this study is to determine the erosion susceptibility occurring in the Çapakçur Stream basin, one of the important erosion areas of Türkiye. In the study, erosion susceptibility analysis was carried out using 4 different methods Shannon Entropy (SE), Logistic Regression (LR), Frequency Ratio (FR) and Weight of Evidence (WoE) that are effectively used today in erosion susceptibility analysis and determination of critical areas in terms of erosion, and 19 conditioning factors based on these methods. Analysis Results Model performances were evaluated using Receiver Operating Characteristic (ROC) and Area under the Curve (AUC) values based on a dataset consisting of 840 training (70%) and 360 testing (30%) points. According to result of the AUC values show that Logistic regression seems to perform well on both training (AUC= 94.7%) and validating datasets (AUC=93.5%). On the other hand, Weight of Evidence training (AUC= 93.5%) and testing datasets (AUC= 91.4%), Frequency Ratio training (AUC= 93.5%) and testing datasets (AUC=92.4%) of the Weight of Evidence result show that AUC and ROC values similar to Logistic Regression result, but slightly lower than Logistic Regression. Additionally, Shannon Entropy shows that it performs lower than other methods on both training (AUC= 55.7%) and testing datasets (AUC= 56.3%). Conducting analyses based on these methods, especially in erosion susceptibility studies, will facilitate both planning and the accuracy of the results obtained.