An Ensemble Broad Learning System (BLS) for Evaluating Landslide Susceptibility in Taiyuan City, Northern China

Landslides are common and highly destructive geological hazards that pose significant threats to both human lives and property on a global scale every year. In this study, a novel ensemble broad learning system (BLS) was proposed for evaluating landslide susceptibility in Taiyuan City, Northern China. Meanwhile, ensemble learning models based on the classification and regression tree (CART) and support vector machine (SVM) algorithms were applied for a comparison with the BLS-AdaBoost model. Firstly, in this study, a grand total of 114 landslide locations were identified, which were randomly divided into two parts, namely 70% for model training and the remaining 30% for model validation. Twelve landslide conditioning factors were selected for mapping landslide susceptibility. Subsequently, three models, namely CART-AdaBoost, SVM-AdaBoost and BLS-AdaBoost, were constructed and used to map landslide susceptibility. The frequency ratio (FR) was used to assess the relationship between landslides and different influencing factors. Finally, the three models were validated and compared on the basis of both statistical-based evaluations and ROC curve-based evaluations. The results showed that the integrated model with BLS as the base learner achieved the highest AUC value of 0.889, followed by the integrated models that used CART (AUC = 0.873) and SVM (AUC = 0.846) as the base learners. In general, the BLS-based integrated learning methods are effective for evaluating landslide susceptibility. Currently, the application of BLS and the integrated BLS model for evaluating landslide susceptibility is limited. This study is one of the first efforts to use BLS and the integrated BLS model for evaluating landslide susceptibility. BLS and its improvements have the potential to provide a more powerful approach to assess landslide susceptibility.