Application of incremental learning in landslide susceptibility assessment: A case study of Tianshui, Gansu Province

To enhance the generalization ability of machine learning models in the assessment of landslide susceptibility, this paper takes the city of Tianshui as an example and employs an incremental learning model based on LightGBM. By utilizing the Autogluon automated machine learning framework, the model's hyperparameter optimization and mdoel stacking are implemented. Additionally, the SHAP explainable framework is used for feature selection and data anomaly analysis. By using the above methods we construct an incremental learning model suitable for landslide susceptibility assessment. Model validation using landslide disaster data collected from various regions in Tianshui city demonstrates that the incremental learning model for landslide susceptibility can effectively identify and predict landslide-prone areas. It adapts to new datasets by self-adjusting the model and improves model performance.