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

Osamah Basheer Shukur


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.


multiple linear regression (MLR); neuro-fuzzy (NF); PM10 datasets; forecasting; ANFIS.

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