Obálka

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

Uloženo v:
Vydáno v:Measurement vol. 166 (Dec 15, 2020), p. 1
Hlavní autor: Chen, Siyu
Další autoři: Gu, Chongshi, Lin, Chaoning, Wang, Yao, Hariri-Ardebili, Mohammad Amin
Vydáno:
Elsevier Science Ltd.
Témata:
Leakage
Sensitivity analysis
Monitoring systems
Qualitative analysis
Water level fluctuations
Risk management
Data mining
Rainfall
Artificial neural networks
Pumped storage
Rockfill dams
Algorithms
Concrete dams
Dam safety
Kernels
Monitoring
Machine learning
Power plants
On-line přístup:Citation/Abstract
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstrakt: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.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.108161
Zdroj:Materials Science Database