ArticleScientific reports2021
Application of RR-XGBoost combined model in data calibration of micro air quality detector.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Calibration of Low-Cost LoRaWAN-Based IoT Air Quality Monitors Using the Super Learner Ensemble: A Case Study for Accurate Particulate Matter Measurement.Sensors (Basel, Switzerland) · 2025Article
- Re-calibrating measurements of low-cost air quality monitors using PCR-GPR air quality forecasting models.PloS one · 2025Article
- A spatiotemporal XGBoost model for PMHeliyon · 2023Article
- A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model.RSC advances · 2023Article
- Calibration of miniature air quality detector monitoring data with PCA-RVM-NAR combination model.Scientific reports · 2022Article
- A data calibration method for micro air quality detectors based on a LASSO regression and NARX neural network combined model.Scientific reports · 2021Article
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Authors and funding
5 authors.
Funding
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Abstract
Grid monitoring is the current development direction of atmospheric monitoring. The micro air quality detector is of great help to the grid monitoring of the atmosphere, so higher requirements are put forward for the accuracy of the micro air quality detector. This paper presents a model to calibrate the measurement data of the micro air quality detector using the monitoring data of the air quality monitoring station. The concentration of six types of air pollutants is the research object of this study to establish a calibration model for the measurement data of the micro air quality detector. The first step is to use correlation analysis to find out the main factors affecting the concentration of the six types of pollutants. The second step uses Ridge Regression (RR) to select variables, find out the factors that have significant effects on the concentration of pollutants, and give the quantitative relationship between these factors and the pollutants. Finally, the predicted value of the ridge regression model and the measurement data of the micro air quality detector are used as input variables, and the Extreme Gradient Boosting (XGBoost) algorithm is used to give the final pollutant concentration prediction model. We named the combined model of ridge regression and XGBoost algorithm RR-XGBoost model. Relative Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), goodness of fit (R
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