出版社：Thai Society of Higher Eduction Institutes on Environment
摘要：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).
