Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation
Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. Th...
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| Format: | Article |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/8/4677 |
| _version_ | 1797606596472209408 |
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| author | Ning Xi Qiang Yang Yingjie Sun Gang Mei |
| author_facet | Ning Xi Qiang Yang Yingjie Sun Gang Mei |
| author_sort | Ning Xi |
| collection | DOAJ |
| description | Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings. |
| first_indexed | 2024-03-11T05:18:29Z |
| format | Article |
| id | doaj.art-f275bded0bfa4258a743e399834e2cb6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:18:29Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f275bded0bfa4258a743e399834e2cb62023-11-17T18:07:35ZengMDPI AGApplied Sciences2076-34172023-04-01138467710.3390/app13084677Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative InvestigationNing Xi0Qiang Yang1Yingjie Sun2Gang Mei3School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, ChinaChina Institute of Geo-Environment Monitoring, Beijing 100081, ChinaCenter for Hydrogeology and Environmental Geology, China Geological Survey, Baoding 071000, ChinaSchool of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, ChinaSlope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings.https://www.mdpi.com/2076-3417/13/8/4677slope deformationtime-series datamachine learninglong-short-term memory (LSTM)transformer |
| spellingShingle | Ning Xi Qiang Yang Yingjie Sun Gang Mei Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation Applied Sciences slope deformation time-series data machine learning long-short-term memory (LSTM) transformer |
| title | Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation |
| title_full | Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation |
| title_fullStr | Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation |
| title_full_unstemmed | Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation |
| title_short | Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation |
| title_sort | machine learning approaches for slope deformation prediction based on monitored time series displacement data a comparative investigation |
| topic | slope deformation time-series data machine learning long-short-term memory (LSTM) transformer |
| url | https://www.mdpi.com/2076-3417/13/8/4677 |
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