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Related Experiment Video

Updated: May 29, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Published on: February 9, 2024

Mine water inflow prediction model based on variational mode decomposition and gated recurrent units optimized by

Juntao Chen^1,2, Mingjin Fan³

¹College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.

Scientific Reports

|February 5, 2025

Summary

This summary is machine-generated.

Accurate mine water prediction is crucial for safety. The novel VMD-iCHOA-GRU model significantly improves prediction accuracy, outperforming existing methods for forecasting water inflow in mines.

Keywords:

Gated recurrent units Improved chimp optimization algorithm Variational mode decomposition Water damage accidents

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Area of Science:

Mining Engineering
Water Resource Management
Artificial Intelligence

Background:

Water damage accidents are frequent in Chinese mines, posing risks to coal resource extraction.
Accurate prediction of incoming water is essential for safe and efficient mining operations.

Purpose of the Study:

To enhance the accuracy of mine water inflow prediction.
To introduce an improved model integrating decomposition, time series prediction, and optimization techniques.

Main Methods:

Proposed the Variational Mode Decomposition-improved Chaotic Grey Wolf Optimizer-Gated Recurrent Unit (VMD-iCHOA-GRU) model.
Utilized Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²) for evaluation.
Compared VMD-iCHOA-GRU against VMD-GRU, iCHOA-GRU, CHOA-GRU, and GRU models.

Main Results:

The VMD-iCHOA-GRU model demonstrated superior performance in predicting water inflow trends.
Achieved evaluation index values of 0.00862 (MAE), 0.01059 (RMSE), 0.02189% (MAPE), and 0.87079 (R²).
Exhibited the smallest MAE, RMSE, MAPE, and the largest R², indicating highest prediction accuracy.

Conclusions:

The VMD-iCHOA-GRU model offers the best prediction effect for mine water inflow.
This advanced model significantly improves prediction accuracy compared to conventional methods.
The findings support the adoption of VMD-iCHOA-GRU for enhanced mine safety and operational efficiency.