Combining LinkNet and Fourier Neural Operator with GPU-Accelerated Computing to Forecast Rock Avalanche Motion

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Abstract
Rock avalanches are composed of large volumes of rapidly flowing rock fragments that can reach velocities exceeding 50 m/s and devastate extensive areas, posing a severe threat to people and infrastructure. Numerical models are essential for forecasting the associated hazards and risks; however, the runtime of most existing models limits their ability. While Deep Learning (DL) models can potentially address this limitation, the efficiency of most numerical models limits the data available for training DL. In the present work, we have overcome this limitation using the GPU-based numerical model ORIN-3D (parallel simulatOR of landslIde motioN over 3D terrain), which provides large quantities of training data for DL. We compare LinkNet and Fourier Neural Operator (FNO) for forecasting the runout of rock avalanches, which accounts for the effects of topography, initial conditions, and path materials. Utilizing ORIN-3D simulations derived from our database comprising 20 case histories and 100 synthetic cases. Both architectures demonstrate strong parameter interpolation on known terrains (especially FNO with a True Positive Rate (TPR): 96.3%, False Positive Rate (FPR): 0.7%). While cross-terrain generalization remains challenging, FNO significantly outperforms U-Net and achieves better generalization performance when temporal dimension is included in the architecture (TPR: 86.0%, FPR: 5.1%). Both architectures achieve significantly higher forecast speeds than ORIN-3D, with FNO being roughly 1000 times faster. This dramatic improvement is crucial for applications such as landslide risk analysis, where large-scale rapid forecasts are essential.

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