A Bidirectional Long Short-Term Neural Network Model to Predict Air Pollutant Concentrations: A Case Study of Tehran, Iran

Document Type : Original Article


1 Assistant Prof, Department of Electrical Engineering, Faculty of Engineering, Vali-E-Asr University of Rafsanjan, Rafsanjan, Iran.

2 Assistant Prof, Department of Biomedical Engineering, Meybod University, Meybod, Iran.

3 Assistant Prof, Department of Electrical Engineering, Adiban Higher Education Institute, Garmsar, Iran.

4 Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj , Iran.


Air pollution is a major challenge of new civilized world, which has intensified as a result of industrialization, urbanization expansion, and rapid traffic growth and increasing anthropogenic activities. Air pollutants can cause serious toxicological impacts on human health and the environment. Therefore, knowledge of pollutant concentrations can be used as key information in pollution control programs and policies. The main aim of this research was to provide a new hybrid model for short-term air quality prediction. Air pollution is the presence of pollutants in the air such as sulphur dioxide (SO2), particle substances (PM), nitrogen oxides (NOX), and ozone (O3). Sulfur dioxide (SO2), particulate matter with particle size less than 10 microns (PM10), particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in the air all have an impact on the Air Quality Index (AQI). The AQI's variation is extremely nonlinear and complex. One of the most desired features of an AQI prediction model is the ability to predict the AQI level that is dangerous to the population. To increase forecast accuracy, an air quality prediction model based on deep recurrent neural networks and the BiLSTM model is created in this research. AQIs (O3, PM2.5, PM10, NO2, SO2, CO) prediction has been examined among several air pollutants. The data on O3, PM2.5, PM10, NO2, SO2, and CO concentrations are average daily reports from the Tarbiat Modares station in Tehran's 6th district, collected between March 2018 and June 2019. The station is located at 35.71751 latitude and 51.385909 longitudes. The proposed model was compared with Shallow Learning and MLP neural networks which had better and more reliable results over the aforementioned neural network in predicting the concentration of pollutants. The regression coefficient obtained to predict the concentrations of pollutants O3, PM2.5, PM10, NO2 and SO2 was 0.87, 0.62, 0.84, 0.67, 0.75 and 0.72, respectively. The results showed that the Bi-LSTM deep neural network model is a reliable method to predict hourly air pollutant concentrations.


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