The study on settlement prediction model of earth-rock dams based on EMD-LSTM

Abstract

In response to the challenges faced by settlement prediction models for earth-rock dams, such as the susceptibility of regression models to multicollinearity, and issues like overfitting, local minima traps, and difficulty in determining hyperparameters in neural network models, an optimized model was proposed based on Empirical Mode Decomposition(EMD) and Long Short-Term Memory(LSTM) neural networks. Firstly, EMD is employed to perform multi-scale decomposition of time series data from Global Navigation Satellite System(GNSS) measurement points, extracting trend and periodic components. Then, Principal Component Analysis(PCA) is utilized to select key influencing factors, reducing data dimensionality and enhancing the generalization capability of the model. Finally, an LSTM is used to construct the time series model, and the Whale Optimization Algorithm(WOA) is applied to optimize the hyperparameters of the LSTM, improving the model′s prediction accuracy and convergence speed. The experimental result show that this model offers significant advantages in the settlement prediction of earth-rock dams, with a Mean Squared Error(MSE) of 7.070 1, a Mean Absolute Error(MAE) of 1.885 9, and a coefficient of determination(R²) of 99.83%. Compared to traditional method, this model demonstrates notable improvements in noise reduction, feature capture, and hyperparameter optimization, providing an accurate and reliable solution for settlement prediction in earth-rock dams.

Keywords

earth-rock dam / settlement prediction / model / empirical mode decomposition(EMD) / long short-term memory(LSTM)