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Research on a Gas Concentration Prediction Algorithm Based on Stacking.

Yonghui Xu1, Ruotong Meng1, Xi Zhao1

  • 1Institute of Automatic Testing and Control, Harbin Institute of Technology, Harbin 150080, China.

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This study introduces a stacking model for improved gas concentration prediction. The novel approach enhances accuracy in detecting toxic and flammable gas mixtures.

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automatic grid search algorithmensemble learningmodel fusionregression algorithm

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Area of Science:

  • Environmental Science
  • Computer Science
  • Chemical Engineering

Background:

  • Machine learning is crucial for identifying hazardous gases.
  • Accurate prediction of mixed gas concentrations remains a challenge.

Purpose of the Study:

  • To enhance the accuracy of gas concentration regression prediction algorithms.
  • To develop a stacking model for improved mixed gas concentration prediction.

Main Methods:

  • A stacking model was developed using stochastic forest, extreme random regression tree, and gradient boosting decision tree (GBDT) as base learners.
  • The grid search algorithm was employed for automatic parameter optimization of the stacking model.

Main Results:

  • The proposed stacking model demonstrated superior prediction performance compared to other ensemble methods.
  • Experimental simulations confirmed a significant improvement in the accuracy of mixed gas concentration prediction.

Conclusions:

  • The stacking model offers a more effective approach for gas concentration prediction.
  • This method enhances the reliability of hazardous gas detection systems.