Investigation of landslide dam life span using prediction models based on multiple machine learning algorithms

Abstract

A rapid and accurate prediction of a landslide dam’s life span is of significant importance for emergency geological treatment. However, current prediction models for the state of a landslide dam are based solely on geomorphological indexes, and do not take into consideration attribute properties such as landslide types, trigger factors, and dam types. This study investigates the relationships between a landslide dam’s geometry and the capacity of the barrier lake and proposes fitting models, which supplement the current landslide dam database. Subsequently, six predictive models for landslide dam life span are established, utilizing machine learning algorithms such as logistic regression, k-nearest neighbors, support vector machine, Naïve Bayes, decision tree, and random forest, which consider five factors, including geometry parameters and attribute properties. The performances of these six models are analyzed and compared to a typical prediction model, the dimensionless blockage index (DBI). The results suggest that the models established in this study not only have a consistent absolute accuracy as the DBI model, but also overcome the disadvantage that a large number of cases cannot be judged by the DBI model. Among the formulated machine learning models, the random forest model exhibits the highest absolute accuracy (89%), lowest error rate (7%), lowest false alarm rate (15%), and no uncertainty rate. Additionally, three renowned landslide dams, namely the Costantino, Hsiaolin, and Baige landslide dams, are analyzed to illustrate the applicability of the established machine learning models. The study results provide essential guidance for the predictions and emergency geological treatments of landslide dam disasters.