To enhance the accuracy of mine water inflow predictions and address the current inability of these forecasts so as to respond promptly to dynamic changes,a multifactorial time-series combination forecast model for mine water inflow was developed,based on modal decomposition and deep learning.The model employs variational modal decomposition and grey relational analysis to select the main con-trolling factors,and predicts high and low frequency modal components through bidirectional long short-term memory networks and convolutional long short-term memory networks.The results that:compared to different time series prediction models,variational modal decomposition can effectively capture long-term dependencies in time series data,thus exhibting a more accurate long-term time series prediction capability;after processing different frequency modal components with whale optimization and Bayesian optimization algorithms,the disorder and complexity of the high-frequency part were effectively re-duced,and the more linear and slow low-frequency part was optimized;through error assessment,the ef-fectiveness and applicability of the variational modal deep learning combination model in mine water inflow time series prediction were verified,and its predictive accuracy meets production requirements.The model has better predictive performance and higher accuracy in the field of mine water inflow pre-diction,and one more method of mine water inflow time series prediction has been added,a theoretical significance for the prevention of water hazards in coal mines.
关键词
模态分解/深度学习/时间序列/多因素序列降维/矿井涌水量预测
Key words
modal decomposition/depth learning/time series/multi-factor series dimensionality reduc-tion/mine water inflow prediction
维普
万方数据