Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides
Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In t...
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MDPI AG
2022-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/481 |
| _version_ | 1797490542738669568 |
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| author | Fasheng Miao Xiaoxu Xie Yiping Wu Fancheng Zhao |
| author_facet | Fasheng Miao Xiaoxu Xie Yiping Wu Fancheng Zhao |
| author_sort | Fasheng Miao |
| collection | DOAJ |
| description | Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R<sup>2</sup> values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters. |
| first_indexed | 2024-03-10T00:34:29Z |
| format | Article |
| id | doaj.art-799ff4b23a4b40f9b711053595492d28 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:34:29Z |
| publishDate | 2022-01-01 |
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| spelling | doaj.art-799ff4b23a4b40f9b711053595492d282023-11-23T15:19:22ZengMDPI AGSensors1424-82202022-01-0122248110.3390/s22020481Data Mining and Deep Learning for Predicting the Displacement of “Step-like” LandslidesFasheng Miao0Xiaoxu Xie1Yiping Wu2Fancheng Zhao3Faculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaLandslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R<sup>2</sup> values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters.https://www.mdpi.com/1424-8220/22/2/481Three Gorges ReservoirBaishuihe landslidedata miningdisplacement predictionVMD-FOA-BPNN |
| spellingShingle | Fasheng Miao Xiaoxu Xie Yiping Wu Fancheng Zhao Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides Sensors Three Gorges Reservoir Baishuihe landslide data mining displacement prediction VMD-FOA-BPNN |
| title | Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides |
| title_full | Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides |
| title_fullStr | Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides |
| title_full_unstemmed | Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides |
| title_short | Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides |
| title_sort | data mining and deep learning for predicting the displacement of step like landslides |
| topic | Three Gorges Reservoir Baishuihe landslide data mining displacement prediction VMD-FOA-BPNN |
| url | https://www.mdpi.com/1424-8220/22/2/481 |
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