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Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets

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@article{IJASEIT12586,
   author = {Osamah Basheer Shukur},
   title = {Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {4},
   year = {2020},
   pages = {1457--1464},
   keywords = {multiple linear regression (MLR); neuro-fuzzy (NF); PM10 datasets; forecasting; ANFIS.},
   abstract = {

The forecasting of time series data is essential by following statistical and intelligent techniques. Particular matter (PM10) is a time series dataset used to scale the air pollution as a dependent variable while there are many types of pollutants used as independent variables. MLR model has been used as a traditional linear approach to forecasting PM10 data. Combining NF as a nonlinear intelligent method with MLR in a hybrid MLR-NF method has been proposed for improving PM10 forecasts and handling the nonlinearity of datasets. The forecasting results reflected that the hybrid method outperformed the traditional method. Although a multiple linear regression (MLR) model has been used for air quality forecasting depending on several meteorological variables in many recent studies, the MLR model is unable to identify the nonlinear pattern of these types of data. Malaysian datasets of PM10 and several climate pollutants will be studied in this paper. The objective of this study is to forecast PM10 and obtain the best results and minimum forecasting error. In this paper, the dependent variable will be forecasted by using traditional and intelligent methods. MLR has been used as a traditional method to forecast PM10. Neuro-fuzzy (NF) in the adapted copy, which calls the adaptive neuro-fuzzy inference system (ANFIS) is combined with MLR and used as an intelligent method to forecast PM10. The results reflect that MLR-NF outperformed MLR for forecasting PM10 data. In conclusion, MLR-NF can be used to forecast PM10 for more accurate results compared to traditional methods.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12586},    doi = {10.18517/ijaseit.10.4.12586} }

EndNote

%A Shukur, Osamah Basheer
%D 2020
%T Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets
%B 2020
%9 multiple linear regression (MLR); neuro-fuzzy (NF); PM10 datasets; forecasting; ANFIS.
%! Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets
%K multiple linear regression (MLR); neuro-fuzzy (NF); PM10 datasets; forecasting; ANFIS.
%X 

The forecasting of time series data is essential by following statistical and intelligent techniques. Particular matter (PM10) is a time series dataset used to scale the air pollution as a dependent variable while there are many types of pollutants used as independent variables. MLR model has been used as a traditional linear approach to forecasting PM10 data. Combining NF as a nonlinear intelligent method with MLR in a hybrid MLR-NF method has been proposed for improving PM10 forecasts and handling the nonlinearity of datasets. The forecasting results reflected that the hybrid method outperformed the traditional method. Although a multiple linear regression (MLR) model has been used for air quality forecasting depending on several meteorological variables in many recent studies, the MLR model is unable to identify the nonlinear pattern of these types of data. Malaysian datasets of PM10 and several climate pollutants will be studied in this paper. The objective of this study is to forecast PM10 and obtain the best results and minimum forecasting error. In this paper, the dependent variable will be forecasted by using traditional and intelligent methods. MLR has been used as a traditional method to forecast PM10. Neuro-fuzzy (NF) in the adapted copy, which calls the adaptive neuro-fuzzy inference system (ANFIS) is combined with MLR and used as an intelligent method to forecast PM10. The results reflect that MLR-NF outperformed MLR for forecasting PM10 data. In conclusion, MLR-NF can be used to forecast PM10 for more accurate results compared to traditional methods.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12586 %R doi:10.18517/ijaseit.10.4.12586 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 4 %@ 2088-5334

IEEE

Osamah Basheer Shukur,"Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 4, pp. 1457-1464, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.4.12586.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Shukur, Osamah Basheer
PY  - 2020
TI  - Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 4
Y2  - 2020
SP  - 1457
EP  - 1464
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - multiple linear regression (MLR); neuro-fuzzy (NF); PM10 datasets; forecasting; ANFIS.
N2  - 

The forecasting of time series data is essential by following statistical and intelligent techniques. Particular matter (PM10) is a time series dataset used to scale the air pollution as a dependent variable while there are many types of pollutants used as independent variables. MLR model has been used as a traditional linear approach to forecasting PM10 data. Combining NF as a nonlinear intelligent method with MLR in a hybrid MLR-NF method has been proposed for improving PM10 forecasts and handling the nonlinearity of datasets. The forecasting results reflected that the hybrid method outperformed the traditional method. Although a multiple linear regression (MLR) model has been used for air quality forecasting depending on several meteorological variables in many recent studies, the MLR model is unable to identify the nonlinear pattern of these types of data. Malaysian datasets of PM10 and several climate pollutants will be studied in this paper. The objective of this study is to forecast PM10 and obtain the best results and minimum forecasting error. In this paper, the dependent variable will be forecasted by using traditional and intelligent methods. MLR has been used as a traditional method to forecast PM10. Neuro-fuzzy (NF) in the adapted copy, which calls the adaptive neuro-fuzzy inference system (ANFIS) is combined with MLR and used as an intelligent method to forecast PM10. The results reflect that MLR-NF outperformed MLR for forecasting PM10 data. In conclusion, MLR-NF can be used to forecast PM10 for more accurate results compared to traditional methods.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12586 DO - 10.18517/ijaseit.10.4.12586

RefWorks

RT Journal Article
ID 12586
A1 Shukur, Osamah Basheer
T1 Using the MLR and Neuro-Fuzzy Methods to Forecast Air Pollution Datasets
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 4
YR 2020
SP 1457
OP 1464
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 multiple linear regression (MLR); neuro-fuzzy (NF); PM10 datasets; forecasting; ANFIS.
AB 

The forecasting of time series data is essential by following statistical and intelligent techniques. Particular matter (PM10) is a time series dataset used to scale the air pollution as a dependent variable while there are many types of pollutants used as independent variables. MLR model has been used as a traditional linear approach to forecasting PM10 data. Combining NF as a nonlinear intelligent method with MLR in a hybrid MLR-NF method has been proposed for improving PM10 forecasts and handling the nonlinearity of datasets. The forecasting results reflected that the hybrid method outperformed the traditional method. Although a multiple linear regression (MLR) model has been used for air quality forecasting depending on several meteorological variables in many recent studies, the MLR model is unable to identify the nonlinear pattern of these types of data. Malaysian datasets of PM10 and several climate pollutants will be studied in this paper. The objective of this study is to forecast PM10 and obtain the best results and minimum forecasting error. In this paper, the dependent variable will be forecasted by using traditional and intelligent methods. MLR has been used as a traditional method to forecast PM10. Neuro-fuzzy (NF) in the adapted copy, which calls the adaptive neuro-fuzzy inference system (ANFIS) is combined with MLR and used as an intelligent method to forecast PM10. The results reflect that MLR-NF outperformed MLR for forecasting PM10 data. In conclusion, MLR-NF can be used to forecast PM10 for more accurate results compared to traditional methods.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12586 DO - 10.18517/ijaseit.10.4.12586