Multi-relation spatiotemporal graph residual network model with multi-level feature attention: A novel approach for landslide displacement prediction
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters. While most existing prediction methods focus on time-series forecasting for individual monitoring points, there is limited research on the spatiotemporal characteristics of landslide deformat...
Saved in:
Main Authors: | Ziqian Wang, Xiangwei Fang, Wengang Zhang, Xuanming Ding, Luqi Wang, Chao Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-07-01
|
Series: | Journal of Rock Mechanics and Geotechnical Engineering |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775524004785 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Representing the Spatiotemporal State Evolution of Geographic Entities as a Multi-Level Graph
by: Feng Yuan, et al.
Published: (2025-06-01) -
EEG Emotion Recognition Using AttGraph: A Multi-Dimensional Attention-Based Dynamic Graph Convolutional Network
by: Shuai Zhang, et al.
Published: (2025-06-01) -
Integrating Time Series Decomposition and Deep Learning: An STL-TCN-Transformer Framework for Landslide Displacement Prediction
by: Shuai Ren, et al.
Published: (2025-02-01) -
A Study on Interpretable Electric Load Forecasting Model with Spatiotemporal Feature Fusion Based on Attention Mechanism
by: Shuaishuai Li, et al.
Published: (2025-05-01) -
Spatiotemporal Interactive Learning for Cloud Removal Based on Multi-Temporal SAR–Optical Images
by: Chenrui Xu, et al.
Published: (2025-06-01)