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This paper implements deep learning methods of recurrent neural networks and short-term memory models. Two kinds of time-series data were used: air pollutant factors, such as O3, SO2, and CO2 from 2017 to 2019, and meteorological factors such as temperature, humidity, wind direction, and wind speed. A trained model was used to predict air pollution within an eight-hour period. Correlation analysis was applied using Pearson and Spearman correlation coefficients. The KNN method was used to fill in the missing values to improve the generated model's accuracy. The average absolute error percentage value was used in the experiments to evaluate the model's performance. LSTM had the lowest RMSE value at 1.9 than the other models from the experiments. CNN had a significant RMSE value at 3.5, followed by Bi-LSTM at 2.5 and Bi-GRU at 2.7. In comparison, the RNN was slightly higher than LSTM at a 2.4 value. [ABSTRACT FROM AUTHOR]

Abstract: Incineration is considered as an efficient approach in dealing with the increasing demand for municipal and industrial solid waste treatment, especially in areas without sufficient land resources. Facing the concern of health risk, the toxic pollutants emitted from incinerators have attracted much attention from environmentalists, even though this technology is capable of reducing solid waste volume and demand for landfill areas, together with plenty of energy generation. To reduce the negative impacts of toxic chemicals emitted from incinerators, various monitoring and control plans are made not only for use in facilities performance evaluation but also better control of operation for stable effluent quality. How to screen out the key variables from massive observed and control variables for modeling the dioxin emission has become an important issue in incinerator operation and pollution prevention. For these reasons, this study used 4-year monitoring data of an incinerator in Taiwan as a case study, and developed a prediction model based on an artificial neural network (ANN) to forecast the dioxin emission. By doing this, a simplified monitoring strategy for incinerators with regarding to dioxin emission control can be achieved. The result indicated that the prediction model based on a back-propagation neural network is a promising method to deal with complex and non-linear data with the help of statistics in screening out the useful variables for modeling. The suitable architecture of an ANN for using in the dioxin prediction consists of 5 input factors, 3 basic layers with 8 hidden nodes. The R 2 was found to equal 0.99 in both the training and testing steps. In addition, sensitivity analysis can identify the most significant variables for the dioxin emission. From the obtained results, the frequency of activated carbon injection showed as the factor of highest relative importance for the dioxin emission. [Copyright &y& Elsevier]