Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China
Accurate landslide displacement prediction has great practical significance for mitigating geohazards. Traditional deterministic forecasting methods can provide only a single point value and cannot give the degree of uncertainty associated with the forecast, thereby failing to provide information on...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2019/8985325 |
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| author | Yankun Wang Huiming Tang Tao Wen Junwei Ma Zongxing Zou Chengren Xiong |
| author_facet | Yankun Wang Huiming Tang Tao Wen Junwei Ma Zongxing Zou Chengren Xiong |
| author_sort | Yankun Wang |
| collection | DOAJ |
| description | Accurate landslide displacement prediction has great practical significance for mitigating geohazards. Traditional deterministic forecasting methods can provide only a single point value and cannot give the degree of uncertainty associated with the forecast, thereby failing to provide information on predictive confidence. This study applied interval prediction for landslide displacement. Taking the Tanjiahe landslide of the Three Gorges Reservoir Area as an example and considering the impact of seasonal variations in reservoir level and rainfall, the uncertainties associated with landslide displacement prediction were quantified into prediction intervals (PIs) by a bootstrapped least-square support vector machine (LSSVM) method (B-LSSVM). The proposed method consists of three steps: First, the LSSVM and bootstrapping were combined to estimate the true regression means of landslide displacement and the variance with respect to model misspecification uncertainties. Second, a new LSSVM model optimized by a genetic algorithm (GA) was implemented to estimate the noise variance. Finally, the point prediction was derived from the regression means, and the PIs were constructed by combining the regression mean, the model variance, and the noise variance. We applied the proposed method to predict the displacement of four GPS monitoring points of the Tanjiahe landslide, and we comprehensively compared the prediction accuracy and the quality of the constructed PIs with benchmark methods. A simulation and performance comparison showed that the proposed method is a promising technique for providing accurate and reliable prediction results for landslide displacement. |
| format | Article |
| id | doaj-art-503ef0b26099470fa19a5ff065aa2f72 |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-503ef0b26099470fa19a5ff065aa2f722025-02-03T05:57:37ZengWileyGeofluids1468-81151468-81232019-01-01201910.1155/2019/89853258985325Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, ChinaYankun Wang0Huiming Tang1Tao Wen2Junwei Ma3Zongxing Zou4Chengren Xiong5Faculty of Engineering, China University of Geosciences, Wuhan, Hubei 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan, Hubei 430074, ChinaSchool of Geosciences, Yangtze University, Wuhan, Hubei 430100, ChinaThree Gorges Research Center for Geo-Hazards of Ministry of Education, China University of Geosciences, Wuhan, Hubei 430074, ChinaThree Gorges Research Center for Geo-Hazards of Ministry of Education, China University of Geosciences, Wuhan, Hubei 430074, ChinaThree Gorges Research Center for Geo-Hazards of Ministry of Education, China University of Geosciences, Wuhan, Hubei 430074, ChinaAccurate landslide displacement prediction has great practical significance for mitigating geohazards. Traditional deterministic forecasting methods can provide only a single point value and cannot give the degree of uncertainty associated with the forecast, thereby failing to provide information on predictive confidence. This study applied interval prediction for landslide displacement. Taking the Tanjiahe landslide of the Three Gorges Reservoir Area as an example and considering the impact of seasonal variations in reservoir level and rainfall, the uncertainties associated with landslide displacement prediction were quantified into prediction intervals (PIs) by a bootstrapped least-square support vector machine (LSSVM) method (B-LSSVM). The proposed method consists of three steps: First, the LSSVM and bootstrapping were combined to estimate the true regression means of landslide displacement and the variance with respect to model misspecification uncertainties. Second, a new LSSVM model optimized by a genetic algorithm (GA) was implemented to estimate the noise variance. Finally, the point prediction was derived from the regression means, and the PIs were constructed by combining the regression mean, the model variance, and the noise variance. We applied the proposed method to predict the displacement of four GPS monitoring points of the Tanjiahe landslide, and we comprehensively compared the prediction accuracy and the quality of the constructed PIs with benchmark methods. A simulation and performance comparison showed that the proposed method is a promising technique for providing accurate and reliable prediction results for landslide displacement.http://dx.doi.org/10.1155/2019/8985325 |
| spellingShingle | Yankun Wang Huiming Tang Tao Wen Junwei Ma Zongxing Zou Chengren Xiong Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China Geofluids |
| title | Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China |
| title_full | Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China |
| title_fullStr | Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China |
| title_full_unstemmed | Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China |
| title_short | Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China |
| title_sort | point and interval predictions for tanjiahe landslide displacement in the three gorges reservoir area china |
| url | http://dx.doi.org/10.1155/2019/8985325 |
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