ENIVRONMENT ASIA

Volume 11, No. 03, Month SEPTEMBER, Year 2018, Pages 251 - 262

Application of exponential smoothing holt winter and arima models for predicting air pollutant concentrations

Arie Dipareza Syafei, Nurul Ramadhan, Joni Hermana, Agus Slamet, Rachmat Boedisantoso and Abdu Fadli Assomadi

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Two time series models, Holt Winter and Autoregressive Integrated Moving Average (ARIMA), were adapted to predict the concentrations of daily air pollutants in Surabaya, Indonesia. Two scenarios were developed to assess model performance in predicting PM10, SO2, CO, NO2, and O3 concentrations. In the first scenario, we used measured data, and, in the second scenario, we tested model performance when the data contained many missing values. We varied the percentage of missing values for three different sets of trained data and filled them with interpolations. It was found that the Holt Winter model was best at predicting CO, NO2, and O3 concentrations using measured data, whereas the ARIMA model was better at predicting PM10 and SO2 concentrations. An assessment of model performance when there were missing values shows that the Holt Winter model was not affected by the number of missing values and missing data patterns in the prediction of CO and O3 concentrations, although it was affected in the prediction of NO2. On the other hand, the ARIMA model, which was used for the prediction of PM10 and SO2 concentrations, was not affected by the amount of missing data and missing data patterns. The Holt Winter model is recommended for the prediction of CO concentrations based on the following model goodness of fit criteria for three different experimental runs with various amounts of missing data: the mean error, ME, (0.039; -0.878; -1106); root mean square error, RMSE, (0.315; 0.985; 1.175); coefficient of determination, R2, (0.516; 0.612; 0.785); and correlation (0.719; 0.782; 0.886).

Keywords

ARIMA; Holt winter; Prediction; Air pollution

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