Novel Integration of JCHAIDStar with ensemble techniques for comprehensive landslide and flash flood susceptibility mapping - Vu Cao, Dat; Pham Thi Thanh, Nga; Tran Hong, Thai; Tran Van, Phong; Mai Van, Khiem; Hoang Van, Dai; Prakash, Indra; Thai Pham, Binh (Vietnam Journal of Earth Sciences, 2025)
This study highlights a novel ensemble approach integrating the JCHAIDStar model with various ensemble techniques namely Dagging (Dag), Bagging (Bag), Decorate (Deco), and Cascade Generalization (CG) for multi-hazard susceptibility assessment and mapping of landslides and flash floods (LS-FF) in Ha Giang province, Vietnam. A total of 963 landslides and 106 flash flood events were used for model development and validation. Flash floods rapidly saturate soil, reducing its cohesion and destabilizing slopes, which leads to landslides. Conversely, landslides may block rivers, creating natural dams that fail abruptly, resulting in flash floods. In this study, a comprehensive dataset comprising 963 landslides, 106 flash floods, and thirteen conditioning factors related to topography, hydrology, geology, and meteorology was utilized. This dataset was split into training (70%) and test (30%) sets for model development and validation, with AUC used to evaluate performance; the Bag-JCHAIDStar model achieved the highest predictive accuracy (AUC = 0.985 for training and 0.951 for testing). The results demonstrated that ensemble-based JCHAIDStar models outperformed single benchmark models (LR and SVM). The generated susceptibility maps provided reliable spatial information for land-use planning and disaster risk mitigation.