1 Department of Civil Engineering, Institute of Engineering and Technology, Lucknow Uttar Pradesh-226021, India.
2 Indian Institute of Tropical Meteorology, New Delhi Branch, New Delhi, 110060, India.
Research Article
World Journal of Advanced Research and Reviews, 2023, 19(03), 1487-1495
Article DOI: 10.30574/wjarr.2023.19.3.1982
Received on 14 August 2023; revised on 26 September 2023; accepted on 28 September 2023
Particulate matter (PM) is a major contributor to air pollution, and exposure to its harmful effects at elevated levels poses a significant risk to human health. Therefore, it is of considerable practical importance for air quality monitoring, air pollution restoration, and human health to have an accurate prediction of PM concentration. Traditional machine learning, neural networks, and deep learning are all areas of focus. In this investigation, we have undertaken a time series analysis on PM2.5 from 2018-2022 at DTU station of Delhi. For doing the time series forecasting, we have used a model called Long Short-Term Memory networks (LSTMs). Mean squared error (MAE), root mean squared error (RMSE), Mean absolute percentage error (MAPE), and Pearson Coefficient (r) are employed to check the validity and applicability of the constructed LSTM model. MSE was obtained as 1832.06, RMSE as 42.80, MAPE as 34.07 and R2 as 0.514. We have predicted PM2.5 values for next 30 days in the year 2023 based on simulated model. The values obtained were ranging from 282.6 µg/m3 to 130.9 µg/m3.
Particulate matter; Neural networks; Prediction; LSTM; PM2.5.
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Pragati Rai, Amarendra Singh, Atul Kumar Srivastava and Prabhat Kumar Patel. Prediction of particulate matter concentrations using LSTM networks model. World Journal of Advanced Research and Reviews, 2023, 19(3), 1487-1495. Article DOI: https://doi.org/10.30574/wjarr.2023.19.3.1982