摘要
为应对尾矿坝位移预测所面临的复杂情况和精度要求,提出一种基于多算法耦合的尾矿坝位移动态预测模型。首先,基于时间序列分解模型将累计位移分为趋势项和周期项,利用高斯回归时间序列预测模型预测趋势项位移;然后,运用不同Copula函数研究诱发因素与周期项位移的整体相关性,鉴于周期项位移影响因素多样性与强非线性的特点,采用多策略融合的改进麻雀搜索算法改进麻雀搜索算法(MISSA)-卷积神经网络(CNN)-双向长短期记忆(BiLSTM)模型预测周期项位移;最后,将高斯回归趋势项位移预测值和MISSA-CNN-BiLSTM周期项位移预测值叠加。结果表明:尾矿坝累积位移预测值与实测值基本一致,预测结果相关性系数R为0.996,均方根误差(RMSE)为0.13 mm,建立的MISSA-CNN-BiLSTM多算法耦合模型预测精度较高,且能较好地预测尾矿坝位移的阶跃型变化。
A comprehensive and sophisticated multi-algorithm coupled dynamic prediction model is proposed to address the intricate reality and stringent accuracy requirements of predicting tailings dam displacement.Firstly,by employing a time series decomposition model,the cumulative displacement is disaggregated into its trend and cyclical components.The trend term displacement is then forecasted using a Gaussian regression time series prediction model.Secondly,various Copula functions are employed to investigate the overall correlation between the inducing factors and the cyclical term displacement.Owing to the diverse influencing factors and strong nonlinearities associated with the cyclical term displacement,the MISSA-CNN-BiLSTM model is utilized for prediction.Lastly,the predicted trend term displacement from the Gaussian regression model and the predicted cyclical term displacement from the MISSA-CNN-BiLSTM model are merged.The results demonstrate a high degree of consistency between the predicted cumulative landslide displacements and the measured values,with a correlation coefficient of 0.996 and a root mean square error(RMSE)of 0.13 mm.The multi-algorithm coupled model,based on MISSA-CNN-BiLSTM,exhibits remarkable prediction accuracy and effectively captures step changes in tailings dam displacements.
作者
刘迪
杨辉
卢才武
阮顺领
江松
LIU Di;YANG Hui;LU Caiwu;RUAN Shunling;JIANG Song(School of Resource Engineering,Xi'an University of Architecture and Technology,Xi'an Shaanxi 710055,China;Xi'an Key Laboratory of Perceptive Computing and Decision for Intelligent Industry,Xi'an University of Architecture and Technology,Xi'an Shaanxi 710055,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2024年第9期145-154,共10页
China Safety Science Journal
基金
国家自然科学基金资助(51208282)
陕西省社会科学基金资助(2023R035)。
