Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods
Само за регистроване кориснике
2020
Чланак у часопису (Објављена верзија)
,
Taylor & Fransis
Метаподаци
Приказ свих података о документуАпстракт
This article presents the results of the statistical modeling of the ground-level ozone concentration in the air in the close vicinity of the city of Zrenjanin (Serbia). This study is aimed at defining the dependence of ozone concentration on the following predictors: SO2, CO, H2S, NO, NO2, NOx, PM10, benzene, toluene, m,p-Xylene, o-Xylene and ethylbenzene concentration in the air, as well as on the meteorological parameters (the wind direction, the wind speed, air pressure, air temperature, solar radiation, and RH). Multiple linear regression analysis (MLRA) and artificial neural networks (ANNs) were used as the tools for the mathematical analysis of the indicated occurrence. The results have shown that ANNs provide better estimates of ozone concentration on the monitoring site, whereas the multilinear regression model once again has proven to be less efficient in the accurate prediction of ozone concentration.
Кључне речи:
Ozone / MLRA / artificial neural networks / pollutants / air qualityИзвор:
OZONE-SCIENCE & ENGINEERING, 2020, 42, 1, 79-88Издавач:
- Taylor & Fransis
Институција/група
Mašinski fakultetTY - JOUR AU - Arsić, Milica AU - Mihajlović, Ivan AU - Nikolić, Đorđe AU - Živković, Živan AU - Panić, Marija PY - 2020 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5296 AB - This article presents the results of the statistical modeling of the ground-level ozone concentration in the air in the close vicinity of the city of Zrenjanin (Serbia). This study is aimed at defining the dependence of ozone concentration on the following predictors: SO2, CO, H2S, NO, NO2, NOx, PM10, benzene, toluene, m,p-Xylene, o-Xylene and ethylbenzene concentration in the air, as well as on the meteorological parameters (the wind direction, the wind speed, air pressure, air temperature, solar radiation, and RH). Multiple linear regression analysis (MLRA) and artificial neural networks (ANNs) were used as the tools for the mathematical analysis of the indicated occurrence. The results have shown that ANNs provide better estimates of ozone concentration on the monitoring site, whereas the multilinear regression model once again has proven to be less efficient in the accurate prediction of ozone concentration. PB - Taylor & Fransis T2 - OZONE-SCIENCE & ENGINEERING T1 - Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods EP - 88 IS - 1 SP - 79 VL - 42 DO - 10.1080/01919512.2019.1598844 ER -
@article{ author = "Arsić, Milica and Mihajlović, Ivan and Nikolić, Đorđe and Živković, Živan and Panić, Marija", year = "2020", abstract = "This article presents the results of the statistical modeling of the ground-level ozone concentration in the air in the close vicinity of the city of Zrenjanin (Serbia). This study is aimed at defining the dependence of ozone concentration on the following predictors: SO2, CO, H2S, NO, NO2, NOx, PM10, benzene, toluene, m,p-Xylene, o-Xylene and ethylbenzene concentration in the air, as well as on the meteorological parameters (the wind direction, the wind speed, air pressure, air temperature, solar radiation, and RH). Multiple linear regression analysis (MLRA) and artificial neural networks (ANNs) were used as the tools for the mathematical analysis of the indicated occurrence. The results have shown that ANNs provide better estimates of ozone concentration on the monitoring site, whereas the multilinear regression model once again has proven to be less efficient in the accurate prediction of ozone concentration.", publisher = "Taylor & Fransis", journal = "OZONE-SCIENCE & ENGINEERING", title = "Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods", pages = "88-79", number = "1", volume = "42", doi = "10.1080/01919512.2019.1598844" }
Arsić, M., Mihajlović, I., Nikolić, Đ., Živković, Ž.,& Panić, M.. (2020). Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods. in OZONE-SCIENCE & ENGINEERING Taylor & Fransis., 42(1), 79-88. https://doi.org/10.1080/01919512.2019.1598844
Arsić M, Mihajlović I, Nikolić Đ, Živković Ž, Panić M. Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods. in OZONE-SCIENCE & ENGINEERING. 2020;42(1):79-88. doi:10.1080/01919512.2019.1598844 .
Arsić, Milica, Mihajlović, Ivan, Nikolić, Đorđe, Živković, Živan, Panić, Marija, "Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods" in OZONE-SCIENCE & ENGINEERING, 42, no. 1 (2020):79-88, https://doi.org/10.1080/01919512.2019.1598844 . .