The eastern margin of the Qinghai-Tibet Plateau is an extreme topography transition zone, and characterized by significant vegetation zonation, in addition to geographic features (such as enormous topographic relief and active tectonics) that control the occurrence of debris flows, which are rapid, surging flows of water-charged clastic sediments moving along a steep channel and are one of the most dangerous mountain hazards in this region. There is thus an urgent need in this region to conduct a regional-scale debris flow susceptibility assessment to determine the spatial likelihood of a debris flow occurrence and guarantee the safety of people and property, in addition to the smooth operation of the Sichuan-Tibet transport corridor. It is, however, a challenging task to estimate the region’s debris flow susceptibility while taking into consideration the comprehensive impacts of vegetation on the occurrence of debris flows, such as the positive effect of root anchoring and the negative effect of vegetation weight loads. In this study, a novel regional-scale susceptibility assessment method was constructed by integrating state-of-the-art machine learning algorithms (such as support vector classification (SVC), random forest (RF), and eXtreme Gradient Boosting (XGB)) with the removing outliers (RO) algorithm and particle swarm optimization (PSO), allowing the impacts of vegetation on debris flow initiation to be integrated with the topographical conditions, hydrological conditions, and geotechnical conditions. This method is finally applied to assess the regional-scale susceptibility of debris flows in the Dadu River basin on the eastern margin of the Qinghai-Tibet Plateau. The study results show that (i) all hybrid machine learning techniques can effectively predict the occurrence of debris flows in the extreme topography transition zone; (ii) the hybrid machine learning technique RO-PSO-SVC has the best performance, and its accuracy (ACC) is 0.946 and the area under the ROC curve (AUC) is 0.981; (iii) the RO-PSO algorithm improves SVC, RF, and XGB performance (according to the ACC value) by 3.84%, 2.59%, and 5.94%, respectively; and (iv) the contribution rate of ecology-related variables is almost only one-tenth that of topography- and hydrology-related factors, according to the factor important analysis for RO-PSO-SVC. Furthermore, debris flow susceptibility maps for the Dadu River basin were created, which can be used to assess and mitigate debris flow hazards.

In recent years, mountainous areas in China have faced frequent geological hazards, including landslides, debris flows, and collapses. Effective simulation of these events requires a solver for shallow water equations (SWEs). Traditional numerical methods, such as finite difference and finite volume, face challenges in discretizing convection flux terms, while theory-based models need to account for various factors such as shock wave capturing and wave propagation direction, demanding a high-level understanding of the underlying physics. Previous deep learning (DL)-based SWE solvers primarily focused on constructing direct input–output mappings, leading to weak generalization properties when terrain data or stress constitutive relations change. To overcome these limitations, this study introduces a novel SWE solver that combines theory and data-driven methodologies. The core idea is to use artificial neural networks to compute convection flux terms, and to reduce modeling complexity. Theory-based modeling is used to tackle complex terrain and friction terms for the purpose of ensuring generalization. Our method surpasses challenges faced by previous DL-based solvers in capturing terrain and stress variations. We validated our solver’s capabilities by comparing simulation results with analytical solutions, real-world disaster cases, and the widely used Massflow software-generated simulations. This comprehensive comparison confirms our solver’s ability to accurately simulate hazard scenarios and showcases strong generalization on varying terrain and land surface friction. Our proposed method effectively addresses DL-based solver limitations while simplifying the complexities of theory-driven numerical methods, offering a promising approach for hazard dynamics simulation.