A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models
Abstract The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evalu...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49899-0 |
| _version_ | 1827581666614312960 |
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| author | Zheng Zhou Cheng Qiu Yufan Zhang |
| author_facet | Zheng Zhou Cheng Qiu Yufan Zhang |
| author_sort | Zheng Zhou |
| collection | DOAJ |
| description | Abstract The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM10, in the latter sets does not improve overall performance, potentially due to multicollinearity between PM10 and PM2.5 variables. The proposed methodology provides valuable insights into soft sensor modeling for air ozone prediction.Among the 72 sensors, sensor NNR[Y]C outperforms all other evaluated sensors, demonstrating exceptional predictive performance with an impressive R2 of 0.8902, low RMSE of 24.91, and remarkable MAE of 19.16. With a prediction accuracy of 81.44%, sensor NNR[Y]C is reliable and suitable for various technological applications. |
| first_indexed | 2024-03-08T22:39:40Z |
| format | Article |
| id | doaj.art-aa17142dddf44ad5a1676ff39825e6bc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T22:39:40Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-aa17142dddf44ad5a1676ff39825e6bc2023-12-17T12:14:46ZengNature PortfolioScientific Reports2045-23222023-12-0113112310.1038/s41598-023-49899-0A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor modelsZheng Zhou0Cheng Qiu1Yufan Zhang2Department of Material and Environmental Engineering, Chengdu Technological UniversityDepartment of Material and Environmental Engineering, Chengdu Technological UniversityDepartment of Material and Environmental Engineering, Chengdu Technological UniversityAbstract The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM10, in the latter sets does not improve overall performance, potentially due to multicollinearity between PM10 and PM2.5 variables. The proposed methodology provides valuable insights into soft sensor modeling for air ozone prediction.Among the 72 sensors, sensor NNR[Y]C outperforms all other evaluated sensors, demonstrating exceptional predictive performance with an impressive R2 of 0.8902, low RMSE of 24.91, and remarkable MAE of 19.16. With a prediction accuracy of 81.44%, sensor NNR[Y]C is reliable and suitable for various technological applications.https://doi.org/10.1038/s41598-023-49899-0 |
| spellingShingle | Zheng Zhou Cheng Qiu Yufan Zhang A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models Scientific Reports |
| title | A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models |
| title_full | A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models |
| title_fullStr | A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models |
| title_full_unstemmed | A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models |
| title_short | A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models |
| title_sort | comparative analysis of linear regression neural networks and random forest regression for predicting air ozone employing soft sensor models |
| url | https://doi.org/10.1038/s41598-023-49899-0 |
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