Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning.

Cheng, Xiang; Li, Qingquan; Zhou, Zhiwei; Luo, Zhixiang; Liu, Ming; Liu, Lu

Cheng, Xiang; Li, Qingquan; Zhou, Zhiwei; Luo, Zhixiang; Liu, Ming; Liu, Lu.

Afiliación

Cheng X; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. xiang.cheng@whu.edu.cn.
Li Q; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. liqq@szu.edu.cn.
Zhou Z; Key Laboratory of Geo-environmental Surveillance in the Maritime and Marine Zones, National Mapping and Geographic Information Bureau, Shenzhen University, Shenzhen 518061, China. liqq@szu.edu.cn.
Luo Z; State Key Laboratory of Geodesy and Earth's Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China. zhiwei.zhou@whigg.ac.cn.
Liu M; Key Laboratory of Geo-environmental Surveillance in the Maritime and Marine Zones, National Mapping and Geographic Information Bureau, Shenzhen University, Shenzhen 518061, China. 2172331945@email.szu.edu.cn.
Liu L; Guizhou Water Conservancy and Hydropower Survey and Design Institute, Guiyang 550000, China. liuming930703@163.com.

Sensors (Basel) ; 18(9)2018 Aug 21.

Article en En | MEDLINE | ID: mdl-30134613

Palabras clave

Nuozhadu; abnormal value judgment; high core-wall rockfill dam seepage; linear regression; osmometer; principal component analysis; seepage control model

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2018 Tipo del documento: Article País de afiliación: China

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