High slope deformation prediction based on residual modified ARIMA-PSO-GRNN models

Qingda Duan; Qingbin Zhang; Yu Huang

doi:10.1117/12.3025623

28 February 2024 High slope deformation prediction based on residual modified ARIMA-PSO-GRNN models

Qingda Duan, Qingbin Zhang, Yu Huang

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130713S (2024) https://doi.org/10.1117/12.3025623
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

In order to improve the prediction accuracy of high slope deformation data, a high slope deformation data prediction model combining autoregressive integrated moving average model (ARIMA), particle swarm optimization algorithm (PSO) and generalized regression neural network (GRNN) is proposed. Considering the nonlinearity and complexity of high slope deformation data, the model uses the ARIMA model for linear prediction, and the PSO-GRNN model corrects the residuals of the ARIMA model for nonlinear prediction. The results show that, by comparing with many prediction models, the residual-corrected ARIMA-PSO-GRNN model has the highest prediction accuracy, with MSE, MAE, and MRE of 0.0500, 0.1373, and 0.8285%, respectively. Using this model for prediction in practical work can provide scientific basis and decision support for related personnel.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingda Duan, Qingbin Zhang, and Yu Huang "High slope deformation prediction based on residual modified ARIMA-PSO-GRNN models", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130713S (28 February 2024); https://doi.org/10.1117/12.3025623

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