Estimating PM2.5 utilizing multiple linear regression and ANN techniques

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作者
Sumita Gulati
Anshul Bansal
Ashok Pal
Nitin Mittal
Abhishek Sharma
Fikreselam Gared
机构
[1] S. A. Jain College,Department of Mathematics
[2] S. A. Jain College,Department of Chemistry
[3] Chandigarh University,Department of Mathematics
[4] University Centre for Research and Development,Department of Computer Engineering and Applications
[5] Chandigarh University,Faculty of Electrical and Computer Engineering
[6] GLA University,undefined
[7] Bahir Dar Institue of Technology,undefined
[8] Bahir Dar University,undefined
来源
Scientific Reports | / 13卷
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摘要
The accurate prediction of air pollutants, particularly Particulate Matter (PM), is critical to support effective and persuasive air quality management. Numerous variables influence the prediction of PM, and it's crucial to combine the most relevant input variables to ensure the most dependable predictions. This study aims to address this issue by utilizing correlation coefficients to select the most pertinent input and output variables for an air pollution model. In this work, PM2.5 concentration is estimated by employing concentrations of sulfur dioxide, nitrogen dioxide, and PM10 found in the air through the application of Artificial Neural Networks (ANNs). The proposed approach involves the comparison of three ANN models: one trained with the Levenberg–Marquardt algorithm (LM-ANN), another with the Bayesian Regularization algorithm (BR-ANN), and a third with the Scaled Conjugate Gradient algorithm (SCG-ANN). The findings revealed that the LM-ANN model outperforms the other two models and even surpasses the Multiple Linear Regression method. The LM-ANN model yields a higher R2 value of 0.8164 and a lower RMSE value of 9.5223.
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