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 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.

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors

“…For instance, Fienberg developed a network of 20 sensor pods to study air quality at the Shelby Farm monitoring site, where three sensors never operated, six failed during operation, and with R 2 threshold of 0.5, only six sensor pods met the data quality objective. Such sensor failure and data loss has been detected from other sensor networks as well [ 48 , 113 , 114 , 115 ]. Outlier detection is defined as the detection of values that are statistically significantly distinct from the other normal values at a given time and location [ 113 ].…”