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16 December 2022 Design of an LSTM model for dam leakage prediction
Jun Zhang, Wenbo Li, Bo Hu, Haiyun Yang, Haoran Wang
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Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 125005E (2022) https://doi.org/10.1117/12.2660683
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China
Abstract
Leakage problems in the process of dam construction and operation affect the structural safety of the dam. To detect these anomalies and provide early warning, modeling and analyzing the leakage monitoring data is necessary. This paper designs an LSTM (Long Short-Term Memory) model to predict the uplift pressure from the leakage data in a real-world dam. The analysis shows that the LSTM model has higher accuracy than the MLR (Multiple Linear Regression), MLP (Multilayer Perceptron) neural networks, SVM (Support Vector Machine), and BRT (Boosted Regression Tree).
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Jun Zhang, Wenbo Li, Bo Hu, Haiyun Yang, and Haoran Wang "Design of an LSTM model for dam leakage prediction", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 125005E (16 December 2022); https://doi.org/10.1117/12.2660683
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KEYWORDS
Data modeling
Performance modeling
Neurons
Process modeling
Safety
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