Transactions on Machine Intelligence

Transactions on Machine Intelligence

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

Document Type : Original Article

Authors
H. Ghayoumi Zadeh 1
A. Fayazi 2
O. Rahmani Seryasat 3
H. Rabiee 4
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, Shams Higher Education Institute, Gorgan, Iran.
4 Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj , Iran.
10.47176/TMI.2022.63
Abstract
Air pollution remains one of the most pressing environmental challenges in modern urban societies, driven by rapid industrialization, accelerated urban expansion, increasing vehicular traffic, and intensified anthropogenic activities. The presence of harmful substances in the atmosphere, including sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), particulate matter (PM₂.₅ and PM₁₀), and carbon monoxide (CO), poses significant health risks and environmental hazards. Monitoring and forecasting pollutant concentrations are critical for effective air quality management and policy-making. This study presents a novel hybrid model based on deep recurrent neural networks, particularly the Bidirectional Long Short-Term Memory (BiLSTM) architecture, for short-term air quality prediction. The model focuses on forecasting the Air Quality Index (AQI), a composite measure influenced by several pollutants with highly nonlinear and complex behavior. Daily average concentration data for O₃, PM₂.₅, PM₁₀, NO₂, SO₂, and CO were collected from the Tarbiat Modares air quality monitoring station in Tehran’s 6th district (latitude 35.71751, longitude 51.385909) over a 15-month period between March 2018 and June 2019. The proposed BiLSTM model demonstrated superior performance compared to traditional shallow learning techniques and Multi-Layer Perceptron (MLP) networks. The regression coefficients (R²) for O₃, PM₂.₅, PM₁₀, NO₂, SO₂, and CO were 0.87, 0.62, 0.84, 0.67, 0.75, and 0.72, respectively. These results highlight the robustness and reliability of the BiLSTM-based approach for accurately predicting pollutant concentrations, thereby supporting timely decision-making in environmental health management.
Keywords
Air Pollution
Predicting
Deep Recurrent Neural Networks
Bidirectional Long Short-Term Model
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Transactions on Machine Intelligence
Volume 5, Issue 2
Spring 2022
Pages 63-76

  • Receive Date 13 January 2022
  • Revise Date 15 February 2022
  • Accept Date 05 June 2022