Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach
The prediction of frictional air resistance using the inherent properties of roadways is of great significance for ventilation network computation and flow regulation in underground mines. This study proposes an improved stacked learning and error correction-based prediction model for the frictional...
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IEEE
2024-01-01
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Online Access: | https://ieeexplore.ieee.org/document/10433509/ |
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author | Zhipeng Qi Ke Gao Dariusz Obracaj Yujiao Liu Keyi Yuan |
author_facet | Zhipeng Qi Ke Gao Dariusz Obracaj Yujiao Liu Keyi Yuan |
author_sort | Zhipeng Qi |
collection | DOAJ |
description | The prediction of frictional air resistance using the inherent properties of roadways is of great significance for ventilation network computation and flow regulation in underground mines. This study proposes an improved stacked learning and error correction-based prediction model for the frictional air resistance of mine airways, called friction factor. A prediction set is established by selecting ten factors, including tunnel spatial features and support forms, with the ventilation resistance coefficient as the label. The improved stacked model consists of two layers. The first layer is the base learning module, which is composed of four components: Principal Components Analysis and Back Propagation (PCA-BP), GA-Projection Pursuit Regression (GA-PPR), Random Forest (RF), LightGBM (LGBM). The second layer is the meta-learning module, which is composed of the Ridge Regression (RR). Compared to traditional stacked models, the improved model first uses the Extreme Gradient Boosting (XG Boost) learner to evaluate the significance of input feature variables to eliminate redundancy and improve accuracy, thus enhancing prediction precision and computational efficiency. Then, the first-layer prediction results are weighted based on the errors of different prediction models in the training set using K-fold cross-validation. Box-Cox transformation is applied to the training set data from the first layer to the second layer to improve prediction normality and homogeneity. The error correction prediction model extracts the historical prediction errors from the meta-learning module and constructs an error prediction model using support vector machines (SVR), which are then combined with the meta-learning results to obtain the final prediction. The improved stacked model is compared with traditional ensemble learning models and single prediction models, and quantified using three metrics: root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). The results demonstrate that the proposed improved model effectively enhances the prediction accuracy of the ensemble learning models, providing a new prediction method for the accurate acquisition of the friction factor of mine airways. |
first_indexed | 2024-03-07T23:41:42Z |
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institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-03-07T23:41:42Z |
publishDate | 2024-01-01 |
publisher | IEEE |
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series | IEEE Access |
spelling | doaj.art-c69b5738560248c3a9bd03cd898c789c2024-02-20T00:00:46ZengIEEEIEEE Access2169-35362024-01-0112248132483010.1109/ACCESS.2024.336549610433509Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning ApproachZhipeng Qi0https://orcid.org/0009-0000-8809-933XKe Gao1Dariusz Obracaj2https://orcid.org/0000-0001-5987-6718Yujiao Liu3Keyi Yuan4College of Safety Science and Engineering, Liaoning Technical University, Huludao, ChinaCollege of Safety Science and Engineering, Liaoning Technical University, Huludao, ChinaFaculty of Civil Engineering and Resource Management, AGH University of Kraków, Kraków, PolandCollege of Safety Science and Engineering, Liaoning Technical University, Huludao, ChinaCollege of Safety Science and Engineering, Liaoning Technical University, Huludao, ChinaThe prediction of frictional air resistance using the inherent properties of roadways is of great significance for ventilation network computation and flow regulation in underground mines. This study proposes an improved stacked learning and error correction-based prediction model for the frictional air resistance of mine airways, called friction factor. A prediction set is established by selecting ten factors, including tunnel spatial features and support forms, with the ventilation resistance coefficient as the label. The improved stacked model consists of two layers. The first layer is the base learning module, which is composed of four components: Principal Components Analysis and Back Propagation (PCA-BP), GA-Projection Pursuit Regression (GA-PPR), Random Forest (RF), LightGBM (LGBM). The second layer is the meta-learning module, which is composed of the Ridge Regression (RR). Compared to traditional stacked models, the improved model first uses the Extreme Gradient Boosting (XG Boost) learner to evaluate the significance of input feature variables to eliminate redundancy and improve accuracy, thus enhancing prediction precision and computational efficiency. Then, the first-layer prediction results are weighted based on the errors of different prediction models in the training set using K-fold cross-validation. Box-Cox transformation is applied to the training set data from the first layer to the second layer to improve prediction normality and homogeneity. The error correction prediction model extracts the historical prediction errors from the meta-learning module and constructs an error prediction model using support vector machines (SVR), which are then combined with the meta-learning results to obtain the final prediction. The improved stacked model is compared with traditional ensemble learning models and single prediction models, and quantified using three metrics: root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). The results demonstrate that the proposed improved model effectively enhances the prediction accuracy of the ensemble learning models, providing a new prediction method for the accurate acquisition of the friction factor of mine airways.https://ieeexplore.ieee.org/document/10433509/Mine airwaysfrictional air resistance coefficientimproved stacking modelcross-validationprediction accuracy |
spellingShingle | Zhipeng Qi Ke Gao Dariusz Obracaj Yujiao Liu Keyi Yuan Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach IEEE Access Mine airways frictional air resistance coefficient improved stacking model cross-validation prediction accuracy |
title | Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach |
title_full | Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach |
title_fullStr | Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach |
title_full_unstemmed | Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach |
title_short | Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach |
title_sort | forecasting the friction factor of a mine airway using an improvement stacking ensemble learning approach |
topic | Mine airways frictional air resistance coefficient improved stacking model cross-validation prediction accuracy |
url | https://ieeexplore.ieee.org/document/10433509/ |
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