Concrete dam deformation prediction method based on improved LSTM deep learning

Abstract

As the most intuitive and reliable monitoring quantity of concrete dams, deformation can comprehensively reflect the service performance of dams in real time. By constructing a real-time prediction model, it has important guiding significance for the identification and response of deformation anomalies in the operation of water conservancy projects. In this paper, a deep learning algorithm: long-term and short-term memory neural network (LSTM), combined with attention mechanism, is used to construct the deformation prediction model of concrete dam. Through engineering examples, the MSE of LSTM model with attention mechanism is 0.69, and the MAE is 0.67. Compared with the stepwise regression model, the recurrent neural network model (RNN) and the LSTM model without attention mechanism, the errors are reduced. LSTM can better mine the long-term and short-term dependencies in deformation sequences, and use the attention mechanism to influence the global and local relationships between factors, highlighting the contribution of main factors to deformation.