Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning
The seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperatur...
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| Format: | Article |
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MDPI AG
2018-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/9/2749 |
| _version_ | 1811301731139584000 |
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| author | Xiang Cheng Qingquan Li Zhiwei Zhou Zhixiang Luo Ming Liu Lu Liu |
| author_facet | Xiang Cheng Qingquan Li Zhiwei Zhou Zhixiang Luo Ming Liu Lu Liu |
| author_sort | Xiang Cheng |
| collection | DOAJ |
| description | The seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperature, filling height, and aging. The traditional research method is to establish a multiple linear regression model to analyze the influence factors of seepage. However, the multicollinearity between these factors affects parameter estimation, and random errors in the data cause the regression model to fail to be established. This paper starts with data collected by an osmometer, uses the 3δ criterion to process the outliers in the sample data, uses the R language to perform principal component analysis on the processed data to eliminate the multicollinearity of the factors, and finally uses multiple linear regression to model and analyze the data. Taking the Nuozhadu high core rockfill dam as an example, the influencing factors of seepage in the construction period and the impoundment period were studied and the seepage was then forecasted. This method provides guidance for further studies of the same type of dam seepage monitoring model. |
| first_indexed | 2024-04-13T07:14:08Z |
| format | Article |
| id | doaj.art-08c232800ccf4034b696f55a8337154e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:14:08Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-08c232800ccf4034b696f55a8337154e2022-12-22T02:56:47ZengMDPI AGSensors1424-82202018-08-01189274910.3390/s18092749s18092749Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine LearningXiang Cheng0Qingquan Li1Zhiwei Zhou2Zhixiang Luo3Ming Liu4Lu Liu5State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory of Geo-environmental Surveillance in the Maritime and Marine Zones, National Mapping and Geographic Information Bureau, Shenzhen University, Shenzhen 518061, ChinaGuizhou Water Conservancy and Hydropower Survey and Design Institute, Guiyang 550000, ChinaGuizhou Water Conservancy and Hydropower Survey and Design Institute, Guiyang 550000, ChinaThe seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperature, filling height, and aging. The traditional research method is to establish a multiple linear regression model to analyze the influence factors of seepage. However, the multicollinearity between these factors affects parameter estimation, and random errors in the data cause the regression model to fail to be established. This paper starts with data collected by an osmometer, uses the 3δ criterion to process the outliers in the sample data, uses the R language to perform principal component analysis on the processed data to eliminate the multicollinearity of the factors, and finally uses multiple linear regression to model and analyze the data. Taking the Nuozhadu high core rockfill dam as an example, the influencing factors of seepage in the construction period and the impoundment period were studied and the seepage was then forecasted. This method provides guidance for further studies of the same type of dam seepage monitoring model.http://www.mdpi.com/1424-8220/18/9/2749high core-wall rockfill dam seepageabnormal value judgmentprincipal component analysislinear regressionosmometerNuozhaduseepage control model |
| spellingShingle | Xiang Cheng Qingquan Li Zhiwei Zhou Zhixiang Luo Ming Liu Lu Liu Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning Sensors high core-wall rockfill dam seepage abnormal value judgment principal component analysis linear regression osmometer Nuozhadu seepage control model |
| title | Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning |
| title_full | Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning |
| title_fullStr | Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning |
| title_full_unstemmed | Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning |
| title_short | Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning |
| title_sort | research on a seepage monitoring model of a high core rockfill dam based on machine learning |
| topic | high core-wall rockfill dam seepage abnormal value judgment principal component analysis linear regression osmometer Nuozhadu seepage control model |
| url | http://www.mdpi.com/1424-8220/18/9/2749 |
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