Reservoir dam monitoring technology by integrating improved ABC algorithm and SVM algorithm

The safe operation of reservoir dams is crucial for the development of human society and economy, but they are easily deformed due to various factors such as climate and water flow, posing a threat to their safe operation. In response to the above issues, this study proposes a reservoir dam deformation monitoring technology based on artificial bee colony algorithm and least squares support vector algorithm. This study proposes optimization strategies to improve the parameter optimization ability of traditional artificial bee colony algorithms by addressing their shortcomings such as randomness, susceptibility to local optima, and insufficient exploration capabilities. On this basis, data preprocessing operations such as singular value removal, Lagrangian interpolation, and wavelet denoising are carried out on the deformation monitoring data of the reservoir dam. The experiment showed that the optimization algorithm achieved optimal and worst values of 0.00E + 00 on the Rastigin and Ackley functions. The maximum absolute deviation of the proposed model was 0.537 mm, the minimum deviation was −0.017 mm, and the maximum and minimum relative errors were 11.26 % and 0.45 %. Comparative verification showed that the MAE, MAPE, and RMSE values of the proposed model were 0.189, 4.82, and 0.256, respectively, which were better than the comparison algorithms.