An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation
Slope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower accuracy and data fluctuation. Obviously, it is neces...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/22/3667 |
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| author | Xiangyu Li Tianjie Lei Jing Qin Jiabao Wang Weiwei Wang Dongpan Chen Guansheng Qian Jingxuan Lu |
| author_facet | Xiangyu Li Tianjie Lei Jing Qin Jiabao Wang Weiwei Wang Dongpan Chen Guansheng Qian Jingxuan Lu |
| author_sort | Xiangyu Li |
| collection | DOAJ |
| description | Slope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower accuracy and data fluctuation. Obviously, it is necessary to establish a combination model to accurately predict slope deformation. Here, we used the GFW-Fisher optimal segmentation method to establish a multi-scale prediction combination model. Our results indicated that the determination coefficient of linear combination model, weighted geometric average model, and weighted harmonic average model was the highest at the surface spatial scale with a large scale, and their determination coefficients were 0.95, 0.95, and 0.96, respectively. Meanwhile, RMSE, MAE and Relative error were used as indicators to evaluate accuracy and the evaluation accuracy of the weighted harmonic average model was the most obvious, with an accuracy of 5.57%, 3.11% and 3.98%, respectively. Therefore, it is necessary to choose the weighted harmonic average model at the surface scale with a large scale as the slope deformation prediction combination model. Meanwhile, our results effectively solve the problems of the prediction results caused by the single model and data fluctuation and provide a reference for the prediction of slope deformation. |
| first_indexed | 2024-03-09T17:56:18Z |
| format | Article |
| id | doaj.art-8a24b89065ab40699f1603f2bdf53311 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T17:56:18Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-8a24b89065ab40699f1603f2bdf533112023-11-24T10:21:01ZengMDPI AGWater2073-44412022-11-011422366710.3390/w14223667An Improved Combination Model for the Multi-Scale Prediction of Slope DeformationXiangyu Li0Tianjie Lei1Jing Qin2Jiabao Wang3Weiwei Wang4Dongpan Chen5Guansheng Qian6Jingxuan Lu7State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resource and Hydropower Research, Beijing 100038, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resource and Hydropower Research, Beijing 100038, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology (CUMTB), Beijing 100083, ChinaChina Electronic Greatwall ShengFeiFan Information System Co., Ltd., Beijing 102200, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resource and Hydropower Research, Beijing 100038, ChinaSlope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower accuracy and data fluctuation. Obviously, it is necessary to establish a combination model to accurately predict slope deformation. Here, we used the GFW-Fisher optimal segmentation method to establish a multi-scale prediction combination model. Our results indicated that the determination coefficient of linear combination model, weighted geometric average model, and weighted harmonic average model was the highest at the surface spatial scale with a large scale, and their determination coefficients were 0.95, 0.95, and 0.96, respectively. Meanwhile, RMSE, MAE and Relative error were used as indicators to evaluate accuracy and the evaluation accuracy of the weighted harmonic average model was the most obvious, with an accuracy of 5.57%, 3.11% and 3.98%, respectively. Therefore, it is necessary to choose the weighted harmonic average model at the surface scale with a large scale as the slope deformation prediction combination model. Meanwhile, our results effectively solve the problems of the prediction results caused by the single model and data fluctuation and provide a reference for the prediction of slope deformation.https://www.mdpi.com/2073-4441/14/22/3667predictioncombination modelfisher optimal segmentationgoodness-of-fit weightmulti-scalehigh slope |
| spellingShingle | Xiangyu Li Tianjie Lei Jing Qin Jiabao Wang Weiwei Wang Dongpan Chen Guansheng Qian Jingxuan Lu An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation Water prediction combination model fisher optimal segmentation goodness-of-fit weight multi-scale high slope |
| title | An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation |
| title_full | An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation |
| title_fullStr | An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation |
| title_full_unstemmed | An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation |
| title_short | An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation |
| title_sort | improved combination model for the multi scale prediction of slope deformation |
| topic | prediction combination model fisher optimal segmentation goodness-of-fit weight multi-scale high slope |
| url | https://www.mdpi.com/2073-4441/14/22/3667 |
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