Abstract:

To address the reliance on a single factor selection approach in traditional dam deformation prediction and the difficulty of comprehensively capturing complex inter-factor relationships among high-dimensional influencing factors, a factor screening method based on the fusion of multiple algorithms was proposed and applied to construct an LSTM model using optimally factors. Specifically, influencing factors were selected separately using the correlation coefficient method, neighborhood component analysis (NCA), and least absolute shrinkage and selection operator (LASSO) technique. The results from these individual methods were subsequently integrated. Since correlations exist among the factors, highly correlated ones were further eliminated using symmetrical uncertainty (SU), thereby an optimized factors set was obtained. The selected factor set was then used to develop a dam deformation prediction model via an LSTM network. A concrete-faced rockfill dam in Xinjiang was used as the case study. The model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), and the coefficient of determination (R²). The results demonstrate that, compared with traditional factor selection methods, the proposed multi-algorithms are integrated and factors with significant influence on dam deformation are comprehensively and accurately identified. MSE is reduced by 20.11%-59.09%, RMSE by 10.61%-36.05%, and MAE by 9.95%-37.86%, and a superior predictive model was obtained, compared with models using conventional factor selection methods. For specific monitoring points, the maximum reductions in MSE, RMSE, and MAE reach 53.5%, 31.9%, and 34.7%, respectively, while the highest R² value attains 0.986 0.

Key words: multi-algorithm fusion, feature selection, long short-term memory(LSTM), dam deformation, prediction model