Search

The conventional numerical solvers for partial differential equations encounter a formidable challenge, as their computational efficiency and accuracy are heavily contingent on grid size. Recently, machine learning (ML) has exhibited substantial promise in addressing partial differential equations. Nevertheless, substantial hurdles persist in practical applications. In this work, we endeavor to establish a deep learning framework founded on the Fourier neural operator (FNO) for resolving the intricacies of simulating real landslide dynamic processes. Our findings demonstrate that the current FNO approach adeptly replicates landslide dynamic processes and boasts exceptional computational efficiency. Additionally, it is noteworthy that this data‐driven ML methodology can seamlessly incorporate data from other experimental sources or numerical simulation techniques. Consequently, this work underscores the significant potential of utilizing ML methodologies to supplant conventional numerical simulation methods. Plain Language Summary: There are great challenges involved in leveraging machine learning methods to learn realistic physical dynamic processes. When it comes to the real landslide movement across intricate terrains, it is meaningful to validate the capacities of machine learning in tackling the complicated problem. This study aims to propose an innovative solution of modeling of landslide dynamic processes from a machine learning perspective. Here, we introduce a data‐driven framework based on Fourier neural operator to predict the dynamic behavior of actual landslides. Following an exhaustive assessment, the superior performance of our suggested model in real landslide situations and its versatility in adapting to landslides across various geographical regions have been confirmed. This study explores a new approach to modeling landslide dynamic processes and highlights the great potential of data‐driven approaches to address dynamic process challenges present in real physical world. Key Points: The data‐driven deep learning method based on FNO can achieve fast prediction of real landslide accumulation processThe proposed data‐driven method for predicting landslide dynamic processes can be extended to new areas after transfer learningWe provide numerical datasets of landslide dynamics, which can serve as the foundational resources for ML‐based landslide forecasting tasks [ABSTRACT FROM AUTHOR]

Wildfires are becoming a global natural disaster under climate change. A worthwhile concern is that the rainfall thresholds that trigger debris flows in catchments affected by recent wildfires are much lower than those that trigger debris flows in catchments with similar environments but that have not experienced wildfires. In this study, we investigated a major post‐fire debris flow event in Xiangjiao catchment triggered by heavy rainfall in 2021. Through the combination of field observation, laboratory experiments and several numerical models (rainfall interception, infiltration, erosion and runoff models), the physical mechanistic analysis and numerical simulation of the post‐fire debris flow in Xiangjiao catchment was carried out. Results show that the simulated deposited area, erosion depth, and the occurrence time of debris‐flow initiation correlate well with the actual situation. It is demonstrated that the numerical simulation method used in this study can be used to effectively describe the characteristics of the initial and propagation stages of the post‐fire debris flow generated by runoff and provide important insights and guidance for the prediction and prevention of debris‐flow disasters in catchments affected by wildfire. Plain Language Summary: Wildfires are becoming more and more common under more frequent extreme weather. After the wildfire, the overall environment would be substantially changed, which will lead to more frequent debris‐flow disasters. Consequences of these changes are worthy to be deeply investigated. In this study, we investigated a major 2021 post‐fire debris flow event in Xiangjiao catchment, China, triggered by heavy rainfall. Through the combination of field observation, laboratory experiments, and numerical models, the physical mechanistic analysis and numerical simulation of the post‐fire debris flow in Xiangjiao catchment was carried out. Key Points: The mechanism of post‐fire debris flows caused by runoff in Xiangjiao catchment is analyzedUsing multiple numerical models and multi‐source data, the 2021 Xiangjiao post‐fire debris flow is simulatedThe numerical simulation results are analyzed and correlated well with the field investigation [ABSTRACT FROM AUTHOR]

This study used a three-dimensional model based on the finite-discrete element method-smoothed particle hydrodynamics (FDEM-SPH) coupling approach to reconstruct the 11 October 2018 Baige landslide that blocked the Jinsha river. The numerical model simulated the dynamic process of the landslide by FDEM, while the SPH simulated the behavior of the river water. The landslide deposit area and the water-eroded area in the FDEM-SPH simulation agree well with the results of the field investigation. According to the simulation, the main duration of the Baige landslide was 80 s. The peak average speed of the landslide was 34.4 m/s, while the local speed of the sliding mass reached a maximum velocity of 70 m/s. The tensile effect and the shear effect caused isotropic dispersive stress which caused the dynamic and violent fragmentation of the landslide. The model also simulated the variation of kinetic energy, the accumulated friction dissipation, and the fracture energy of the sliding mass with time. After the sliding mass ran into the river, the landslide triggered a wave, which rapidly dispersed due to the subsequent mass movement. The front part of the sliding mass pushed some of the river water to the opposite bank where the wave reached a height of 120 m. The results of the FDEM-SPH model, the particle flow code (PFC) model, and the depth-integrated shallow-water flow (DISWF) model are compared and analyzed concerning studying landslides that block rivers. Three types of evolution mechanisms of high-level flow-like landslide-induced waves in deep river valleys are proposed in this paper. • A novel fluid-solid coupling approach based on FDEM-SPH is used for landslide blocking river. • Dynamic process of the Baige landslide blocking river is shown clearly. • Evolution modes of high-level fluid-like landslides blocking rivers in deep valley area are proposed. [ABSTRACT FROM AUTHOR]