Prediction of Landslide Dam Formation Using Machine Learning Techniques

Predicting landslide dam formation is essential in mitigating landslide risks in alpine valley regions. This study assesses the landslide damming probability with the consideration of landslide characteristics, valley topography, and hydrological factors using machine learning techniques. A landslide inventory is collected, including both damming landslides and non-damming landslides in the 2008 Wenchuan earthquake region and the Bailong River basin. Three machine learning algorithms are compared, including logistic regression, random forest, and support vector machine. Results show that machine learning techniques can well predict the landslide damming probability. The random forest model achieves the best prediction performance, followed by logistic regression and support vector machine. Among six learning features, landslide area, upstream watershed area, and valley floor width are the three most important variables for landslide dam formation. An illustration example of the Tangjiashan landslide dam is used to demonstrate how the developed model can be integrated to predict landslide dam formation.