Flood hazard mapping using M5 tree algorithms and logistic regression; a case study in East Black Sea Region

Abstract

Flood is a type of disaster that occurs as a result of the overflow of the stream outside its bed. Similarly to many parts of the globe, particularly the Eastern Black Sea Region of Turkey is frequently exposed to major floods. The heavy rainfall and topographic structure of the region and the proximity of settlements to stream beds are the primary causes of flooding. The present study pertains to the utilization of the Logistic Regression (LR), M5P Rule Tree (M5PRT) and M5P Regression Tree (M5PRGT) models for the assessment of the flood hazard areas in and around the Of district, located on the Black Sea coast of Trabzon province. According to flood inventory, 16 flood events occurred in 5 different locations in the study area. These areas were converted into point data, and comprising a total of 1600 points, 800 flooded and 800 non-flooded, were determined by random sampling. Accordingly, flood hazard maps were created with 8 flood parameters and 3 different methods. Accuracies of these models were evaluated through AUC (Receiver Operating Characteristics Curve), ACC (Accuracy), R (Recall), P (Precision) and F (F-Score). Analyses showed that the Tree-Based Algorithms are more successful than the LR method in detecting the flood hazards. In addition, the altitude and precipitation were found out to be the most influential parameters in all 3 methods on the occurrence of flooding events in the region. The confluence points of the streams, the coastal plain where the stream disembogues to the sea and the valley floors in and around the Of district were designated as the areas with high risk of flooding.