A COMPARATIVE STUDY ON EDICTIVE EFFECT OF PM<sub>2.5</sub> IN BEIJING BASED ON TREE MODELS

LI Zhi-sheng; LIANG Xi-guan; JIN Yu-kai; ZHANG Hua-gang; OU Yao-chun

doi:10.13205/j.hjgc.202106016

Volume 39 Issue 6

Jan. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2021 > 39(6): 106-113

ZHOU Yu-qi, XU Jun-chao, ZHUO Gui-hua, CHEN Jian-yong, LIU Chang-qing. INFLUENCE OF SLUDGE INOCULATION VOLUME ON METHANOGENESIS OF RESIDUE FROM ANAEROBIC FERMENTATIVE HYDROGEN PRODUCTION USING COMBINED SLUDGE AND FOOD WASTE UNDER MEDIUM TEMPERATURE CONDITION[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(6): 144-149. doi: 10.13205/j.hjgc.202106021

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LI Zhi-sheng, LIANG Xi-guan, JIN Yu-kai, ZHANG Hua-gang, OU Yao-chun. A COMPARATIVE STUDY ON EDICTIVE EFFECT OF PM_2.5 IN BEIJING BASED ON TREE MODELS[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(6): 106-113. doi: 10.13205/j.hjgc.202106016

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A COMPARATIVE STUDY ON EDICTIVE EFFECT OF PM_2.5 IN BEIJING BASED ON TREE MODELS

doi: 10.13205/j.hjgc.202106016

School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China

Received Date: 2020-07-22
Available Online: 2022-01-18

Abstract

Abstract

In urban air quality forecast, the mass concentrations of PM_2.5 were influenced by the meteorological conditions and time period. This article selected Beijing as the experimental area, analysing a variety of pollutants concentration characteristics, time characteristics and weather characteristics. The data by hour of 33 air quality monitoring stations in 2019 were used to carry out the PM_2.5 forecast experiments, based on characteristics of LightGBM(light gradient boosting machine) PM_2.5 mass concentration prediction model. The results showed that compared with random forests model(RF), gradient boosting decision tree model(GBDT), XGBoost model, LightGBM model had the highest prediction accuracy of PM_2.5 concentration, XGBoost model came next, random forest model was the lowest. The accuracy of LightGBM model PM_2.5 prediction was higher than other models, R² was 0.9614, and training LightGBM model was fast and RAM needed less. LightGBM model on the five indicators were better than the rest of the model, and LightGBM model on PM_2.5 hourly prediction had better stability and application prospects.
- periodic characteristics,
- machine learning,
- influencing factors of PM_2.₅,
- LightGBM,
- PM_2.5 prediction

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References(20)

References

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