Success of machine learning and statistical methods in predicting landslide hazard: the case of Elazig (Maden)

Abstract

Landslide hazards affect the security of human life and property. Landslide hazard maps are essential for landslide prevention and mitigation. In this study, the success of machine learning and statistical methods in predicting landslide hazards in and around the district center of Maden, Elazığ province, within the borders of Turkey, was analyzed, and their performances were compared. The Random Forest method correctly predicted 1.398 of the 1.425 landslide points in the training dataset, but was incorrect on 27 points. The same method predicted 1942 of the 2075 landslide-free points in the training dataset, but incorrectly predicted 133 points as landslide-exposed. As a result of the study, it is evident that the Random Forest and M5P Rule Tree methods yield more successful results than the Frequency Ratio method. In the study area, the landslide hazard is concentrated in areas close to the East Anatolian Fault and in areas with steep slopes. Lithology, slope, and seismicity have been identified as important triggering factors for landslides in the region. It is expected that machine learning methods, which operate with high levels of accuracy, will make a significant contribution to the prediction of landslide hazards.