Hazardous Gas Sensor Correction System Based on Linear Regression Algorithm

In this paper, the $\mathbf{C O}$ sensor correction is completed in Wanliu area as an example, so as to achieve accurate measurement of CO in Wanliu area. First, we extracted CO data from the Wanliu area and integrated the data collected by the actual sensors. Subsequently, we calculated the root mean square error between the predicted and actual values to evaluate the accuracy of the prediction model. The results show that we have successfully established a comprehensive CO concentration dataset and derived a reference value for $C O$ concentration. Through the analysis of the integrated data set, we can further understand the relationship between $C O$ concentration and other variables, such as temperature and humidity, and predict the trend of CO concentration. These findings provide valuable information for understanding the $C O$ pollution situation, analyzing the relevant factors, and making corresponding decisions. This research has important implications for environmental protection and public health, and provides a basis for future research.

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Published
2024-10-25
DOI
https://doi.org/10.1109/isstc63573.2024.10824209
Primary Topic
Advanced Sensor and Control Systems
Type
article
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article

Hazardous Gas Sensor Correction System Based on Linear Regression Algorithm

D.-W. Duan
Advanced Sensor and Control Systems
article

Hazardous Gas Sensor Correction System Based on Linear Regression Algorithm

D.-W. Duan
article en

Abstract

In this paper, the $\mathbf{C O}$ sensor correction is completed in Wanliu area as an example, so as to achieve accurate measurement of CO in Wanliu area. First, we extracted CO data from the Wanliu area and integrated the data collected by the actual sensors. Subsequently, we calculated the root mean square error between the predicted and actual values to evaluate the accuracy of the prediction model. The results show that we have successfully established a comprehensive CO concentration dataset and derived a reference value for $C O$ concentration. Through the analysis of the integrated data set, we can further understand the relationship between $C O$ concentration and other variables, such as temperature and humidity, and predict the trend of CO concentration. These findings provide valuable information for understanding the $C O$ pollution situation, analyzing the relevant factors, and making corresponding decisions. This research has important implications for environmental protection and public health, and provides a basis for future research.

Dalian University of Technology (CN)
Climate action
Openalex Percentile: Top 70%
Advanced Sensor and Control Systems
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