Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm
It is increasingly important to create a healthier indoor environment for office buildings. Accurate and reliable prediction of PM2.5 concentration can effectively alleviate the delay problem of indoor air quality control system. The rapid development of machine learning has provided a research basi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Built Environment |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2023.1207193/full |
| _version_ | 1797768755687718912 |
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| author | Zhenwei Guo Zhenwei Guo Xinyu Wang Liang Ge |
| author_facet | Zhenwei Guo Zhenwei Guo Xinyu Wang Liang Ge |
| author_sort | Zhenwei Guo |
| collection | DOAJ |
| description | It is increasingly important to create a healthier indoor environment for office buildings. Accurate and reliable prediction of PM2.5 concentration can effectively alleviate the delay problem of indoor air quality control system. The rapid development of machine learning has provided a research basis for the indoor air quality system to control the PM2.5 concentration. One approach is to introduce the CatBoost algorithm based on rank lifting training into the classification and prediction of indoor PM2.5 concentration. Using actual monitoring data from office building, we consider previous indoor PM2.5 concentration, indoor temperature, relative humidity, CO2 concentration, and illumination as input variables, with the output indicating whether indoor PM2.5 concentration exceeds 25 μg/m3. Based on the CatBoost algorithm, we construct an intelligent classification prediction model for indoor PM2.5 concentration. The model is evaluated using actual data and compared with the multilayer perceptron (MLP), gradientboosting decision tree (GBDT), logistic regression (LR), decision tree (DT), and k-nearest neighbors (KNN) models. The CatBoost algorithm demonstrates outstanding predictive performance, achieving an impressive area under the ROC curve (AUC) of 0.949 after hyperparameters optimition. Furthermore, when considering the five input variables, the feature importance is ranked as follows: previous indoor PM2.5 concentration, relative humidity, CO2, indoor temperature, and illuminance. Through verification, the prediction model based on CatBoost algorithm can accurately predict the indoor PM2.5 concentration level. The model can be used to predict whether the indoor concentration of PM2.5 exceeds the standard in advance and guide the air quality control system to regulate. |
| first_indexed | 2024-03-12T20:58:51Z |
| format | Article |
| id | doaj.art-42362ccb489647e0a32982ce009508fd |
| institution | Directory Open Access Journal |
| issn | 2297-3362 |
| language | English |
| last_indexed | 2024-03-12T20:58:51Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Built Environment |
| spelling | doaj.art-42362ccb489647e0a32982ce009508fd2023-07-31T10:00:57ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622023-07-01910.3389/fbuil.2023.12071931207193Classification prediction model of indoor PM2.5 concentration using CatBoost algorithmZhenwei Guo0Zhenwei Guo1Xinyu Wang2Liang Ge3Chinese Society for Urban Studies, Beijing, ChinaNational Engineering Research Center of Building Technology, Beijing, ChinaChinese Society for Urban Studies, Beijing, ChinaChinese Society for Urban Studies, Beijing, ChinaIt is increasingly important to create a healthier indoor environment for office buildings. Accurate and reliable prediction of PM2.5 concentration can effectively alleviate the delay problem of indoor air quality control system. The rapid development of machine learning has provided a research basis for the indoor air quality system to control the PM2.5 concentration. One approach is to introduce the CatBoost algorithm based on rank lifting training into the classification and prediction of indoor PM2.5 concentration. Using actual monitoring data from office building, we consider previous indoor PM2.5 concentration, indoor temperature, relative humidity, CO2 concentration, and illumination as input variables, with the output indicating whether indoor PM2.5 concentration exceeds 25 μg/m3. Based on the CatBoost algorithm, we construct an intelligent classification prediction model for indoor PM2.5 concentration. The model is evaluated using actual data and compared with the multilayer perceptron (MLP), gradientboosting decision tree (GBDT), logistic regression (LR), decision tree (DT), and k-nearest neighbors (KNN) models. The CatBoost algorithm demonstrates outstanding predictive performance, achieving an impressive area under the ROC curve (AUC) of 0.949 after hyperparameters optimition. Furthermore, when considering the five input variables, the feature importance is ranked as follows: previous indoor PM2.5 concentration, relative humidity, CO2, indoor temperature, and illuminance. Through verification, the prediction model based on CatBoost algorithm can accurately predict the indoor PM2.5 concentration level. The model can be used to predict whether the indoor concentration of PM2.5 exceeds the standard in advance and guide the air quality control system to regulate.https://www.frontiersin.org/articles/10.3389/fbuil.2023.1207193/fullindoor environmentPM2.5 limitCatBoost modelclassification predictionmachine learning |
| spellingShingle | Zhenwei Guo Zhenwei Guo Xinyu Wang Liang Ge Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm Frontiers in Built Environment indoor environment PM2.5 limit CatBoost model classification prediction machine learning |
| title | Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm |
| title_full | Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm |
| title_fullStr | Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm |
| title_full_unstemmed | Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm |
| title_short | Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm |
| title_sort | classification prediction model of indoor pm2 5 concentration using catboost algorithm |
| topic | indoor environment PM2.5 limit CatBoost model classification prediction machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fbuil.2023.1207193/full |
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