Coupling of quantile regression into boosted regression trees (BRT) technique in forecasting emission model of PM10 concentration
نویسندگان
چکیده
Abstract Air pollution is currently becoming a significant global environmental issue. The sources of air in Malaysia are mobile or stationary. Motor vehicles one the sources. Stationary originated from emissions caused by urban development, quarrying and power plants petrochemical. most noticeable contaminant Peninsular particulate matter (PM 10 ), highest contributor Pollution Index (API) compared to other parameters. aim this study determine best loss function between quantile regression (QR) ordinary least squares (OLS) using boosted tree (BRT) for prediction PM concentration Alor Setar, Klang Kota Bharu, Malaysia. Model comparison statistics coefficient determination (R 2 accuracy (PA), index agreement (IA), normalized absolute error (NAE) root mean square (RMSE) show that QR slightly better than OLS with performance R (0.60–0.73), PA (0.78–0.85), IA (0.86–0.92), NAE (0.15–0.17) RMSE (9.52–22.15) next-day predictions BRT model.
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ژورنال
عنوان ژورنال: Air Quality, Atmosphere & Health
سال: 2021
ISSN: ['1873-9318', '1873-9326']
DOI: https://doi.org/10.1007/s11869-021-01045-3