Design of an LSTM model for dam leakage prediction

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).