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Mine Gas Concentration Forecasting Model Based on an Optimized BiGRU Network.

Rong Liang1,2, Xintan Chang1, Pengtao Jia2

  • 1Department of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054 China.

ACS Omega
|November 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an effective deep learning model, Adamax-BiGRU, for accurate mine gas concentration forecasting. The proposed method significantly reduces prediction errors compared to existing models, enhancing mine safety.

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Area of Science:

  • Data science
  • Artificial intelligence
  • Mining engineering

Background:

  • Mine gas concentration monitoring is crucial for safety.
  • Existing forecasting models have limitations in accuracy and utilization of data.
  • Deep learning offers potential for improved predictive capabilities.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for enhanced mine gas concentration forecasting.
  • To improve the utilization of mine gas concentration monitoring data.
  • To compare the proposed model's performance against established methods.

Main Methods:

  • Data preprocessing using Laida criterion and Lagrange interpolation.
  • Development of a bidirectional gated recurrent unit (BiGRU) neural network.
  • Optimization of the BiGRU model using the adaptive moment estimation maximum (Adamax) algorithm.
  • Mean Squared Error (MSE) as the loss function for parameter determination.

Main Results:

  • The Adamax-BiGRU model demonstrated superior performance in gas concentration forecasting.
  • Error reduction of 25.58% compared to Recurrent Neural Network (RNN).
  • Error reduction of 12.53% compared to Long Short-Term Memory (LSTM).
  • Error reduction of 3.01% compared to Gated Recurrent Unit (GRU).
  • The Adamax optimization algorithm yielded the best forecasting outcomes.

Conclusions:

  • The proposed Adamax-BiGRU model is an effective method for predicting mine gas concentration.
  • The model shows significant improvements in accuracy over existing deep learning approaches.
  • This method holds considerable practical value for enhancing mine safety and operational efficiency.