Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine. (15th December 2020) - Chen, Siyu; Gu, Chongshi; Lin, Chaoning; Wang, Yao; Hariri-Ardebili, Mohammad Amin
Highlights: A KELM-based model for the analysis of dam leakage flow is developed. The daily water level variation and lag effects are considered for leakage flow modeling. An efficient optimization framework for adaptive search of model hyperparameters is presented. A global sensitivity analysis method for model interpretation and evaluation is proposed. The model performance is compared with MLR, ELM and RF. Abstract: 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.