TY - JOUR
T1 - Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine
AU - Chen, Siyu
AU - Gu, Chongshi
AU - Lin, Chaoning
AU - Wang, Yao
AU - Hariri-Ardebili, Mohammad Amin
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Dam monitoring
KW - Global sensitivity analysis
KW - Kernel extreme learning machine
KW - Leakage
KW - Optimization
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85088371608&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2020.108161
DO - 10.1016/j.measurement.2020.108161
M3 - Article
AN - SCOPUS:85088371608
SN - 0263-2241
VL - 166
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
M1 - 108161
ER -