Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring
Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynami...
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4754 |
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| author | Jiangfeng Li Jiahao Qin Kaimin Kang Mingzhi Liang Kunpeng Liu Xiaohua Ding |
| author_facet | Jiangfeng Li Jiahao Qin Kaimin Kang Mingzhi Liang Kunpeng Liu Xiaohua Ding |
| author_sort | Jiangfeng Li |
| collection | DOAJ |
| description | Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology first employs the Maximum Overlap Discrete Wavelet Transform (MODWT) to denoise raw Global Navigation Satellite System (GNSS)-monitored displacement time series data, enhancing the underlying deformation features. Subsequently, a geology-aware graph is constructed, using the temporal correlation of displacement series as a practical proxy for physical relatedness between monitoring nodes. The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. Experiments conducted on a real-world dataset from an active open-pit mine demonstrate the framework’s superior performance. The DCRNN-proposed model achieved the highest accuracy among eight competing models, recording a Root Mean Square Error (RMSE) of 2.773 mm in the Vertical direction, a 39.1% reduction compared to its baseline. This study validates that the proposed dynamic graph optimization approach provides a robust and significantly more accurate solution for landslide prediction in complex, real-world engineering environments. |
| format | Article |
| id | doaj-art-c8b64f7ad9c5453fb1fa458fd38cf7c4 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c8b64f7ad9c5453fb1fa458fd38cf7c42025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-08-012515475410.3390/s25154754Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS MonitoringJiangfeng Li0Jiahao Qin1Kaimin Kang2Mingzhi Liang3Kunpeng Liu4Xiaohua Ding5School of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221008, ChinaSchool of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221008, ChinaSchool of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221008, ChinaXinjiang Jiangna Mining Co., Ltd., Hami 839300, ChinaXinjiang Jiangna Mining Co., Ltd., Hami 839300, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221008, ChinaLandslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology first employs the Maximum Overlap Discrete Wavelet Transform (MODWT) to denoise raw Global Navigation Satellite System (GNSS)-monitored displacement time series data, enhancing the underlying deformation features. Subsequently, a geology-aware graph is constructed, using the temporal correlation of displacement series as a practical proxy for physical relatedness between monitoring nodes. The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. Experiments conducted on a real-world dataset from an active open-pit mine demonstrate the framework’s superior performance. The DCRNN-proposed model achieved the highest accuracy among eight competing models, recording a Root Mean Square Error (RMSE) of 2.773 mm in the Vertical direction, a 39.1% reduction compared to its baseline. This study validates that the proposed dynamic graph optimization approach provides a robust and significantly more accurate solution for landslide prediction in complex, real-world engineering environments.https://www.mdpi.com/1424-8220/25/15/4754landslide displacement predictiondynamic graph optimizationGNSS-monitored displacement signal processingspatiotemporal analysisgraph neural networks |
| spellingShingle | Jiangfeng Li Jiahao Qin Kaimin Kang Mingzhi Liang Kunpeng Liu Xiaohua Ding Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring Sensors landslide displacement prediction dynamic graph optimization GNSS-monitored displacement signal processing spatiotemporal analysis graph neural networks |
| title | Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring |
| title_full | Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring |
| title_fullStr | Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring |
| title_full_unstemmed | Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring |
| title_short | Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring |
| title_sort | enhanced spatiotemporal landslide displacement prediction using dynamic graph optimized gnss monitoring |
| topic | landslide displacement prediction dynamic graph optimization GNSS-monitored displacement signal processing spatiotemporal analysis graph neural networks |
| url | https://www.mdpi.com/1424-8220/25/15/4754 |
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