Air Pollution Forecasting using Hybrid MLR-RNN Method with Time-Stratified Method
Abstract
Particular matter (PM10) studying and forecasting is necessary to control and reduce the damage of environment and human health. There are many pollutants as sources of air pollution may effect on PM10 variable. This type of dataset can be classified as nonlinear. Studied datasets have been taken from climate station in Malaysia. Multiple linear regression (MLR) is used as linear statistical method for PM10 forecasting through its influencing by corresponding climate variables, therefore it may reflect inaccurate results when used with nonlinear datasets. To improve the results of forecasting, recurrent neural network (RNN) has been suggested to be used after combining with MLR in hybrid in this study. Wavelet analysis is proposed filtering the result of MLR-RNN method for more improving of forecasting results through RNN-Wavelet hybrid method based on MLR model. In general, the best results of forecasting were for using RNN-Wavelet method. . In addition, the results of hybrid methods were outperformed comparing to MLR model as traditional method. As conclusion in this study, Wavelet analysis can be used after hybridizing with RNN based on MLR as active approach to obtain better forecasting results with nonlinear datasets in which PM10 is to dependent variable.
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