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• W4250654371 abstract "Abstract The issue of air quality has attracted more and more attention. The main methods for monitoring the concentration of pollutants in the air include national monitoring station monitoring and micro air quality detector testing. Since the electrochemical sensor of the micro air quality detector is susceptible to interference, the monitored data has a certain deviation. In this paper, the combined model of partial least square regression and random forest regression (PLS-RFR) is used to correct the detection data of the micro air quality detector. First, correlation analysis is used to find out the factors that affect the concentration of pollutants. Second, partial least squares regression is used to give the quantitative relationship of the influence of each influencing factor on the concentration of pollutants. Finally, the predicted value of partial least squares regression and various influencing factors are used as independent variables, and the pollutant concentration monitored by the national monitoring station is used as the dependent variable, and the PLS-RFR model is obtained with the help of random forest software package. Relative Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), goodness of fit ( R 2 ), and Root Mean Square Error (RMSE) are used as evaluation indicators to compare PLS-RFR model, support vector machine model and multilayer perceptron neural network. The results show that no matter which evaluation index, the prediction effect of the PLS-RFR model is the best, and the model has a good prediction effect in the training set or the test set, indicating that the model has good generalization ability. This model can play an active role in the promotion and deployment of micro air quality detectors." @default.
• W4250654371 created "2022-05-12" @default.
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• W4250654371 date "2021-02-26" @default.
• W4250654371 modified "2023-10-12" @default.
• W4250654371 title "Research on data correction method of micro air quality detector based on combination of partial least squares and random forest regression" @default.
• W4250654371 doi "https://doi.org/10.21203/rs.3.rs-241776/v1" @default.
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