Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy

Abstract Accurate landslide susceptibility mapping (LSM) is critical to risk management, especially in areas with significant development. Although the receiver operating characteristic–area under the curve (ROC–AUC) performance metrics are commonly used to measure model effectiveness, showed that these are not enough to check the reliability of the generated maps. In this study, the effectiveness of three machine learning models—logistic regression (LR), random forest (RF), and support vector machine (SVM)—were evaluated and compared in predicting landslide risk in a hilly region east of Cairo, Egypt. A comprehensive dataset was gathered to achieve that, including 183 landslide and 183 non-landslide…

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  Debreceni Egyetem