基于EMD-GAELM-ARIMA算法的大坝变形预测

Dam Deformation Prediction Based on EMD-GAELM-ARIMA Algorithm

In view of the fact that it is difficult for statistical models to make good predictions of nonlinear and non-stationary dam deformation, artificial intelligence algorithms are induced. The empirical mode decomposition method (EMD), genetic algorithm (GA) optimized extreme learning machine (ELM), and ARIMA error correction model were used to construct a dam deformation prediction model. First this paper uses EMD to decompose and reconstruct the monitoring data to stabilize it and obtain eigenmode functions and residual sequences with physical significance; then uses GAELM to analyze and predict the decomposition results; finally, uses ARIMA model to correct errors. Taking a concrete rockfill dam as an example, the dam deformation prediction model constructed by the optimization algorithm is used to analyze and predict it. The analysis results show that the EMD-GAELM-ARIMA model algorithm has higher prediction accuracy than the traditional single algorithm. It is feasible in dam deformation prediction.