Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines
Accurate and reliable predictions of landslide displacements are difficult to perform using traditional point prediction approaches due to the associated uncertainty. Prediction intervals are effective tools for quantifying the uncertainty of point predictions by estimating the limit of future lands...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/7082594 |
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| author | Yankun Wang Huiming Tang Tao Wen Junwei Ma |
| author_facet | Yankun Wang Huiming Tang Tao Wen Junwei Ma |
| author_sort | Yankun Wang |
| collection | DOAJ |
| description | Accurate and reliable predictions of landslide displacements are difficult to perform using traditional point prediction approaches due to the associated uncertainty. Prediction intervals are effective tools for quantifying the uncertainty of point predictions by estimating the limit of future landslide displacements. In this paper, under the framework of the original lower upper bound estimation method, a direct interval prediction approach is proposed for landslide displacements based on the least squares support vector machine (LSSVM) and differential search algorithms. Two LSSVM models are directly implemented to generate the interval of future displacements, and the optimal model parameters are derived by the differential search algorithm. The Baishuihe landslide and the Tanjiahe landslide located on the shoreline of the Three Gorges Reservoir, China, are used to test the proposed approach. Compared with other models, the proposed method performed best and presented the smallest coverage width-based criterion values of 0.8927 and 1.0562 at monitoring stations XD01 and ZG118 for the Baishuihe landslide, respectively, and 0.1316 and 0.1191 at monitoring stations ZG289 and ZG287 for the Tanjiahe landslide, respectively. The results indicate that the proposed approach can provide high-quality prediction intervals for landslide displacements in the Three Gorges Reservoir area. |
| format | Article |
| id | doaj-art-a51cfff4d08f45b18031a25cb19bf1e0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a51cfff4d08f45b18031a25cb19bf1e02025-02-03T01:04:14ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/70825947082594Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector MachinesYankun Wang0Huiming Tang1Tao Wen2Junwei Ma3Faculty 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 430074, ChinaThree Gorges Research Centre for Geo-Hazards of Ministry of Education, China University of Geosciences, Wuhan, Hubei 430074, ChinaAccurate and reliable predictions of landslide displacements are difficult to perform using traditional point prediction approaches due to the associated uncertainty. Prediction intervals are effective tools for quantifying the uncertainty of point predictions by estimating the limit of future landslide displacements. In this paper, under the framework of the original lower upper bound estimation method, a direct interval prediction approach is proposed for landslide displacements based on the least squares support vector machine (LSSVM) and differential search algorithms. Two LSSVM models are directly implemented to generate the interval of future displacements, and the optimal model parameters are derived by the differential search algorithm. The Baishuihe landslide and the Tanjiahe landslide located on the shoreline of the Three Gorges Reservoir, China, are used to test the proposed approach. Compared with other models, the proposed method performed best and presented the smallest coverage width-based criterion values of 0.8927 and 1.0562 at monitoring stations XD01 and ZG118 for the Baishuihe landslide, respectively, and 0.1316 and 0.1191 at monitoring stations ZG289 and ZG287 for the Tanjiahe landslide, respectively. The results indicate that the proposed approach can provide high-quality prediction intervals for landslide displacements in the Three Gorges Reservoir area.http://dx.doi.org/10.1155/2020/7082594 |
| spellingShingle | Yankun Wang Huiming Tang Tao Wen Junwei Ma Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines Complexity |
| title | Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines |
| title_full | Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines |
| title_fullStr | Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines |
| title_full_unstemmed | Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines |
| title_short | Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines |
| title_sort | direct interval prediction of landslide displacements using least squares support vector machines |
| url | http://dx.doi.org/10.1155/2020/7082594 |
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