###### Abstract  
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. broad learning system (BLS); ensemble machine learning; landslide susceptibility; Northern China 2023  
1 23 8409 11 48409 11 by varying degrees of stability and instability, thereby offering invaluable support for effective landslide risk management and urban development [3]. In recent decades, numerous approaches have been suggested for the preparation of landslide susceptibility maps. With the advent of quantitative research on geological hazards, statistical methods were first used for landslide susceptibility assessments. Methods such as analytic hierarchy process [4], bivariate and multivariate statistical approaches [5; 6; 7], logistic regression [8] and the multivariate adaptive regression spline [9; 10; 11] have been widely used by scholars for mapping landslide susceptibility. Hybrid methods, including frequency ratio bivariate statistical models, weights-of-evidence bivariate statistical models, bivariate statistic-based kernel logistic regression (KLR) models and the bivariate model of the Dempster-Shafer evidential belief function (EBF), have also been proposed and widely applied to analyses of landslide susceptibility [12; 13; 14]. As landslides are a complex geological hazard, a single statistically based approach cannot work well in all areas. Therefore, more sophisticated machine learning algorithms were considered. Nowadays, a variety of machine learning methods are being utilized to prepare landslide susceptibility maps, including support vector machines (SVMs), decision trees [15], K-nearest neighbors (KNN) [16], Extremely Randomized Tree (EXT) [17], Bayesian classifiers, artificial neural networks [18], the adaptive neuro-fuzzy inference system (ANFIS) [19] and convolutional neural networks (CNNs) [20]. Among these, deep learning, as a new machine learning method proposed to overcome the limitations of machine learning algorithms, has been is verified to have good performance in evaluating landslide susceptibility [21]. In areas with fewer landslide data, better results can also be achieved through a transfer learning-based approach [22].