Search

Lagrangian particle-based smoothed particle hydrodynamics (SPH) is increasingly widely used in landslide modelling. This paper investigates four important issues not addressed by previous studies on SPH modelling of large-scale landslides, i.e., convergence property, influence of constitutive parameters, scale effect and friction reduction, and influence of different treatments of the viscous effect. The GPU-acceleration technique is employed to achieve high-resolution three-dimensional (3D) modelling. The Baige landslide is investigated by comparing numerical results with field data, and detailed analyses on the four issues are provided. Suggestions on particle resolution, constitutive parameter, and formulations of viscous discretization are also presented for future SPH modelling of large-scale landslides. Key words: large-scale landslides, smoothed particle hydrodynamics, constitutive model, graphics processing unit (GPU) acceleration, convergence. L'hydrodynamique particulaire lissee (SPH) basee sur des particules lagrangiennes est de plus en plus utilisee dans la modelisation des glissements de terrain. Cet article analyse quatre questions importantes qui n'ont pas ete abordees par les etudes precedentes sur la modelisation SPH des glissements de terrain a grande echelle, a savoir la propriete de convergence, l'influence des parametres constitutifs, de l'effet d'echelle et de la reduction du frottement, et l'influence de differents traitements de l'effet visqueux. La technique d'acceleration par GPU est utilisee pour realiser une modelisation 3D a haute resolution. Le glissement de terrain de Baige est etudie en comparant les resultats numeriques avec les donnees de terrain, et des analyses detaillees sur les quatre questions sont fournies. Des suggestions sur la resolution des particules, les parametres constitutifs et les formulations de la discretisation visqueuse sont egalement presentees pour la modelisation SPH future des glissements de terrain a grande echelle. [Traduit par la Redaction] Mots-cles : glissements de terrain a grande echelle, hydrodynamique des particules lissees, modele constitutif, acceleration par processeur graphique (GPU), convergence., 1. Introduction Large-scale landslides are a major socio-economic threat in mountainous regions, as they usually lead to continuous damage of infrastructures and, in many cases, causalities. Various numerical methods were [...]

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]

The efficiency and accuracy of grid-based computational fluid dynamics methods are strongly dependent on the chosen cell size. The computational time increases exponentially with decreasing cell size. Therefore, a grid coarsing technology without apparent precision loss is essential for various numerical modeling methods. In this article, a physical adaption neural network (PANN) is proposed to optimize coarse grid representation from a fine grid. A new convolutional neural network is constructed to achieve a significant reduction in computational cost while maintaining a relatively accurate solution. An application to numerical modeling of dynamic processes in landslides is firstly carried out, and better results are obtained compared to the baseline method. More applications in various flood scenarios in mountainous areas are then analyzed. It is demonstrated that the proposed PANN downscaling method outperforms other currently widely used downscaling methods. The code is publicly available and can be applied broadly. Computing by PANN is hundreds of times more efficient, meaning that it is significant for the numerical modeling of various complicated Earth-surface flows and their applications. [ABSTRACT FROM AUTHOR]

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptive flood modeling and forecasting framework that can perform at large scales, namely FloodCast. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the multi-satellite observation module, a real-time unsupervised change detection method and a rainfall processing and analysis tool are proposed to harness the full potential of multi-satellite observations in large-scale flood prediction. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced, benefiting from the absence of a requirement for training data in physics-informed neural networks (PINNs) and featuring a fast, accurate, and resolution-invariant architecture with Fourier neural operators. To adapt to complex river geometries, we reformulate PINNs in a geometry-adaptive space. GeoPINS demonstrates impressive performance on popular partial differential equations across regular and irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. This model employs sequence-to-sequence learning and hard-encoding of boundary conditions. Next, we establish a benchmark dataset in the 2022 Pakistan flood using a widely accepted finite difference numerical solution to assess various flood simulation methods. Finally, we validate the model in three dimensions - flood inundation range, depth, and transferability of spatiotemporal downscaling - utilizing SAR-based flood data, traditional hydrodynamic benchmarks, and concurrent optical remote sensing images. Traditional hydrodynamics and sequence-to-sequence GeoPINS exhibit exceptional agreement during high water levels, while comparative assessments with SAR-based flood depth data show that sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with smaller simulation errors. The experimental results for the 2022 Pakistan flood demonstrate that the proposed method enables high-precision, large-scale flood modeling with an average MAPE of 14.93 % and an average Mean Absolute Error (MAE) of 0.0610 m for 14-day water depth simulations while facilitating real-time flood hazard forecasting using reliable precipitation data., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiaoxiang Zhu reports financial support was provided by German Federal Ministry of Education and Research. Xiaoxiang Zhu reports financial support was provided by German Research Foundation., (Copyright © 2024. Published by Elsevier Ltd.)