Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine

被引:79
作者
Chen, Siyu [1 ,4 ]
Gu, Chongshi [1 ,2 ,3 ]
Lin, Chaoning [1 ,6 ]
Wang, Yao [4 ]
Hariri-Ardebili, Mohammad Amin [4 ,5 ]
机构
[1] Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing, Peoples R China
[2] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Peoples R China
[3] Hohai Univ, Natl Engn Res Ctr Water Resources Efficient Utili, Nanjing, Peoples R China
[4] Univ Colorado, Dept Civil Environm & Architectural Engn, Boulder, CO 80309 USA
[5] Univ Maryland, College Pk, MD 20742 USA
[6] Delft Univ Technol, Fac Technol Policy & Management, Delft, Netherlands
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Kernel extreme learning machine; Optimization; Dam monitoring; Leakage; Prediction; Global sensitivity analysis; RELIABILITY-ANALYSIS; REGRESSION; SEEPAGE; MODEL; DEFORMATION;
D O I
10.1016/j.measurement.2020.108161
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The magnitude of leakage in the dam body and its foundation can be used as an important indicator in dam risk management. This study presents a data mining and monitoring framework for safety control of the dam leakage flow. First, the influencing factors in dam leakage flow are investigated. Second, a kernel extreme learning machine (KELM) is trained to predict dam leakage, where the parameters are optimized adaptively by parallel multi-population Jaya algorithm. Finally, a novel global sensitivity analysis is proposed to evaluate the relative importance of each input variable based on the KELM. Monitoring data of leakage flow from the concrete face rockfill dam in a pumped-storage power station is used for modeling and verification. The simulated results of the case study reveal that KELM achieves a satisfactory prediction of the leakage flow. It is also found that the water level fluctuation and rainfall have a significant impact on leakage magnitude. The sensitivity analysis provides a useful qualitative metric of dam leakage, which is of great value for dam safety monitoring and operation. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:13
相关论文
共 40 条
[1]   Seepage and dam deformation analyses with statistical models: support vector regression machine and random forest [J].
Belmokre, Ahmed ;
Mihoubi, Mustapha Kamel ;
Santillan, David .
3RD INTERNATIONAL CONFERENCE ON STRUCTURAL INTEGRITY (ICSI 2019), 2019, 17 :698-703
[2]  
Breiman L., 2001, IEEE Trans. Broadcast., V45, P5
[3]   Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement [J].
Chen, Siyu ;
Gu, Chongshi ;
Lin, Chaoning ;
Zhang, Kang ;
Zhu, Yantao .
ENGINEERING WITH COMPUTERS, 2021, 37 (03) :1943-1959
[4]  
Chen XD, 2013, FRESEN ENVIRON BULL, V22, P500
[5]  
Cheng L., 2018, J EARTHQ ENG, P1
[6]   Modeling wine preferences by data mining from physicochemical properties [J].
Cortez, Paulo ;
Cerdeira, Antonio ;
Almeida, Fernando ;
Matos, Telmo ;
Reis, Jose .
DECISION SUPPORT SYSTEMS, 2009, 47 (04) :547-553
[7]   Statistical analysis and structural identification in concrete dam monitoring [J].
De Sortis, A. ;
Paoliani, P. .
ENGINEERING STRUCTURES, 2007, 29 (01) :110-120
[8]   Extreme learning machine: algorithm, theory and applications [J].
Ding, Shifei ;
Zhao, Han ;
Zhang, Yanan ;
Xu, Xinzheng ;
Nie, Ru .
ARTIFICIAL INTELLIGENCE REVIEW, 2015, 44 (01) :103-115
[9]   Variable Importance Assessment in Regression: Linear Regression versus Random Forest [J].
Groemping, Ulrike .
AMERICAN STATISTICIAN, 2009, 63 (04) :308-319
[10]   Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems [J].
Hariri-Ardebili, Mohammad Amin ;
Salazar, Fernando .
SOFT COMPUTING, 2020, 24 (15) :11583-11604