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Air Quality Forecast and Evaluation Based on Long Short-Term Memory Network and Fuzzy Algorithm

Accurate forecasting of air quality enables the public to formulate effective preventive measures in advance to maintain their health. The Long Short-Term Memory (LSTM) network is used to measure the three major pollutants in Taiwan over the years: PM2.5, O3, and PM10 for the cross-hour Direct Forecast (DF) and Update Forecast (UF) that can only predict the next hour. A new comprehensive evaluation method is proposed by combining the predicted value of LSTM with the fuzzy algorithm (LSTM-Fuzzy) so that the public can understand the future air quality profile more easily. The results from the experiments present that the accuracy of DF usually decreases as the prediction time increases, but the accuracy of UF is relatively stable compared to the DF. Root Mean Squared Error (RMSE) or Mean Absolute Error (MAE) becomes less than 10 or even less than 5 in all seasons, only the prediction ability of summer-traffic air quality monitoring stations is relatively unsatisfactory, with an RMSE of 19.96 and an MAE of 15.42. The accuracy rate of the proposed LSTM-Fuzzy is 100% within 12 hours under four seasons and four monitoring stations, while 100% accuracy within 16 hours in summer, autumn and winter.