A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models

• Published in: Scientific reports Vol. 13; no. 1; pp. 22420 - 23
• Main Authors: Zhou, Zheng, Qiu, Cheng, Zhang, Yufan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.12.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1046; 639/705/531; 704/172/169; Accuracy; Comparative analysis; Humanities and Social Sciences; multidisciplinary; Neural networks; Ozone; Particulate matter; Predictions; Regression analysis; Science; Science (multidisciplinary); Sensors

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 i...