A new predictive model for mine water inflow, known as VMD-iCHOA-GRU, has been developed to improve the safety and efficiency of coal extraction operations within Chinese coal mines. The model employs advanced techniques, including variational mode decomposition (VMD) and gated recurrent units (GRUs), optimized through an improved chimp optimization algorithm (iCHOA), to accurately forecast water inflow trends. This innovation addresses the frequent water damage accidents observed in mining operations across the country.
Water hazards present significant challenges to coal mining, where unforeseen inflows can lead to accidents and increased operational risks. The predictive accuracy of water inflow models plays a pivotal role in averting accidents caused by water influx. Recognizing the limitations of traditional statistical and shallow machine learning methods, which often rely heavily on random factors and display weak performance with complex datasets, this study shifts the focus to deep learning models like GRUs, which possess enhanced capabilities for feature extraction from large-scale data.
The authors, affiliated with China Coal Xinji Energy Co., Ltd. and led by Mingjin Fan, implemented the VMD method to effectively decompose the oscillatory inflow data series before forecasting. The innovative aspect of their approach lies within the iCHOA, which improves upon the standard chimp optimization algorithm by incorporating the Sobol sequence for initial population distribution and utilizing cosine dynamics to widen the search range. By optimizing GRU model hyperparameters, the VMD-iCHOA-GRU model achieved remarkable performance metrics, including the lowest mean absolute error (MAE) of 0.00862, root mean square error (RMSE) of 0.01059, and mean absolute percentage error (MAPE) of 0.02189%. It also presented the highest coefficient of determination (R2) value at 0.87079, confirming its superior predictive accuracy over other models tested during the research.
"The VMD-iCHOA-GRU model has the best prediction effect on the trend of water inflow, achieving the smallest MAE and RMSE among the compared models," noted the authors of the article. Their research involved rigorous testing against various alternatives, such as GRU models without decomposition and unimproved optimization techniques. The findings strongly suggest the necessity of incorporating advanced decomposition techniques alongside optimization strategies to resolve the complex issues posed by mine water inflow predictions.
Recognizing the oscillatory nature of inflow data, which can distort predictions and mislead decision-making processes, this study serves as both significant theoretical and practical advancement within the field. The VMD method exhibited powerful feature extraction capabilities, reducing fluctuations influenced by cluttered data signals. This model could greatly aid miners by providing timely information on water inflow trends, thereby ensuring safer working conditions and preventing potential disasters.
Furthering the conversation, the authors plan to adjust the model parameters based on changing geological conditions to maintain its effectiveness over time. Future research will also include examining additional factors contributing to inflow dynamics, such as aquifer levels and seismic activities, potentially leading to more comprehensive and reliable predictive models.
Overall, the implementation of the VMD-iCHOA-GRU model not only showcases the potential of combining advanced analytical techniques but also highlights the urgent need for improved safety protocols within mining operations. This study indicates promising avenues for enhancing the safety and productivity of coal extraction, offering constructive recommendations for future technological developments directed at mitigating water-related risks.