Prediction of SO2 Concentration Based on AR-LSTM Neural Network

Abstract

Sulphur dioxide is one of the most common air pollutants, forming acid rain and other harmful substances in the atmosphere, which can further damage our ecosystem and cause respiratory diseases in humans. Therefore, it is essential to monitor the concentration of sulphur dioxide produced in industrial processes in real-time to predict the concentration of sulphur dioxide emissions in the next few hours or days and to control them in advance. To address this problem, we propose an AR-LSTM analytical forecasting model based on ARIMA and LSTM. Based on the sensor’s time series data set, we preprocess the data set and then carry out the modeling and analysis work. We analyze and predict the proposed analysis and prediction model in two data sets and conduct comparative experiments with other comparison models based on the three evaluation indicators of R 2 , RMSE and MAE. The results demonstrated the effectiveness of the AR-LSTM analytical prediction model; Finally, a forecasting exercise was carried out for emissions in the coming weeks using our proposed AR-LSTM analytical forecasting model.

A deep learning method for the prediction of focused waves in a wave flume

“…The LSTM unit comprises of four interconnected elements, including the input and control signals for the input, forgetting, and output gates. These components work together to regulate the memory storage, retention, and output, the details of which can be found in [24,25]. The internal structure of the LSTM unit [27].…”