Deep Learning Enables Transferable Rheological Parameters for Landslide Runout Prediction
Authors/Creators
Description
This dataset contains numerical simulation results and deep-learning-based predictions used to investigate transferable rheological parameters for landslide runout modeling. The data were generated using a physics-based shallow water equation (SWE) framework coupled with a deep neural network (DNN) to calibrate μ(I) friction law parameters. Included files provide optimized rheological parameters, model performance metrics, and sensitivity analyses for multiple landslide scenarios under varying topographic and material conditions. These data support the analysis of parameter generalizability and model transferability across different landslide events and catchments.
Abstract (English)
This dataset supports the study “Deep Learning Enables Transferable Rheological Parameters for Landslide Runout Prediction”.
It includes experimental measurements, numerical simulation outputs, deep-learning datasets, and optimized rheological parameters used for model calibration, validation, and transferability analysis across multiple landslide and granular-flow scenarios.
The dataset contains:
- Processed experimental and literature-derived datasets for μ–I relationships across plane shear, ring shear, chute flow, and landslide-scale conditions;
- Numerical simulation outputs for landslide runout modeling;
- Deep learning input and output data used for rheological parameter inversion;
- Optimized parameter sets for multiple landslide events;
- Performance evaluation metrics and transferability analysis results.
File formats, descriptions, and their correspondence to the manuscript figures are provided in the accompanying README.These data support the analyses reported in the manuscript. The README specifies the files available for each figure.
Files
DNN_bidirectional_inset.csv
Files
(907.3 kB)
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