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Treffer: Submit to AGU-Manuscript-Enhancing Landslide Displacement Prediction Using a Spatio-Temporal Deep Learning Model with Interpretable Features

Title:
Submit to AGU-Manuscript-Enhancing Landslide Displacement Prediction Using a Spatio-Temporal Deep Learning Model with Interpretable Features
Authors:
Jia Wang, Honghu Zhu, Wei Zhang, Daoyuan Tan, Alessandro Pasuto
Publication Year:
2025
Subject Terms:
Other earth sciences not elsewhere classified, Reservoir landslide, Spatial-Temporal Graph Neural Network, Attention mechanism, Landslide prediction
Document Type:
dataset
Language:
unknown
DOI:
10.6084/m9.figshare.28171343.v1
Availability:
https://doi.org/10.6084/m9.figshare.28171343.v1
https://figshare.com/articles/dataset/Submit_to_AGU-Manuscript-Enhancing_Landslide_Displacement_Prediction_Using_a_Spatio-Temporal_Deep_Learning_Model_with_Interpretable_Features/28171343
Rights:
CC BY 4.0
Accession Number:
edsbas.CED05F1F
Database:
BASE

Weitere Informationen

This dataset supports the study titled “Enhancing Landslide Displacement Prediction Using a Spatio-Temporal Deep Learning Model with Interpretable Features.” It includes the monitoring data and model prediction results in two Excel files, along with the corresponding Python code used in the study. Specifically, the code covers STL decomposition, cross-correlation function (CCF) calculation, and displacement prediction using ST-GNN, GRU, and LSTM models. This dataset is intended to support reproducibility and facilitate further research.