LIU J, YANG X, GAO H X,et al.A mine water inflow prediction model based on SSA and multivariate time-series fusion[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(5):125-135.

doi:10.16186/j.cnki.1673-9787.2024100020

Received:2024/10/14

Revised:2025/06/10

Published:2026-07-24

A mine water inflow prediction model based on SSA and multivariate time-series fusion

Liu Jie1,2, Yang Xiao1, Gao Hongxing2, Ma Ming2, He Zhiyong3, Ren Yan4

1.College of Earth Science and Engineering， Shandong University of Science and Technology， Qingdao  266590，  Shandong， China；2.Tengzhou Guozhuang Mining Co.， Ltd.， Tengzhou  277519， Shandong，  China；3.Shandong Energy Group Xibei Mining Co.， Ltd.， Xi'an  710026， Shaanxi， China；4.China Railway 25th Bureau Group Third Engineering Co.， Ltd.， Changsha  410001， Hunan， China

Abstract: Objectives To address the difficulty in obtaining hydrogeological parameters in deterministic prediction methods under complex conditions， and the limitations of uncertainty-based approaches in characterizing variations in mine water inflow， a prediction model based on SSA and multivariate time series fusion was developed.  Methods Based on 20 years of mine water inflow data from the study area， descriptive statistical analyses were performed to examine the central tendency， dispersion， and distribution features of the inflow series. Singular spectrum analysis （SSA） was applied to decompose the series into trend， fluctuation， and noise components， and different models were adopted to forecast each component. First， based on the iterative imputation concept of SSA， inner and outer loop algorithms were implemented in MATLAB. The number of iterations was determined using residuals and correlation coefficients between adjacent reconstructed sequences， thereby establishing the trend forecasting model. Second，  according to the characteristics of fluctuation and noise components， ARIMA and SARIMA models selected based on the Bayesian information criterion （BIC） were employed for prediction， and model performance was evaluated using white-noise tests. Finally， the final prediction results were obtained by linearly combining the outputs of the three models.  Results The coefficient of determination （R²） and mean absolute percentage error （MAPE） indicate that the proposed fusion model achieved anR² of 0.94 and a MAPE of 2.34%， outperforming the single-model approach （R²=0.74， MAPE =6.8%）. Conclusions Compared with the single model （R²=74%， MAPE =6.8%）， the multi-method fusion prediction model achieves an R² of 94% and a reduced MAPE of 2.34%. The fitting accuracy is significantly improved， enabling a more accurate characterization of the temporal variation characteristics of mine water inflow in the study area.

Key words:mine water;mine water inflow prediction;data analysis;singular spectrum analysis;time-series analysis