On the Application of Long Short-Term Memory Neural Network for Daily Forecasting of PM2.5 in Dakar, Senegal (West Africa)

Ahmed Gueye (), Serigne Abdoul Aziz Niang, Ismaila Diallo (), Mamadou Simina Dramé, Moussa Diallo and Ali Ahmat Younous
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Ahmed Gueye: Department of Physics, Faculty of Science and Technology, Cheikh Anta Diop of Dakar University (UCAD), Dakar-Fann, Dakar BP 5085, Senegal
Serigne Abdoul Aziz Niang: Department of Physics, Faculty of Science and Technology, Cheikh Anta Diop of Dakar University (UCAD), Dakar-Fann, Dakar BP 5085, Senegal
Ismaila Diallo: Department of Meteorology and Climate Science, San José State University (SJSU), San Jose, CA 95192, USA
Mamadou Simina Dramé: Department of Physics, Faculty of Science and Technology, Cheikh Anta Diop of Dakar University (UCAD), Dakar-Fann, Dakar BP 5085, Senegal
Moussa Diallo: Higher Polytechnic School, University Cheikh Anta Diop of Dakar, Fann, Dakar BP 5005, Senegal
Ali Ahmat Younous: Department of Physics, Faculty of Science and Technology, Cheikh Anta Diop of Dakar University (UCAD), Dakar-Fann, Dakar BP 5085, Senegal

Sustainability, 2025, vol. 17, issue 12, 1-25

Abstract: This study aims to optimize daily forecasts of the PM2.5 concentrations in Dakar, Senegal using a long short-term memory (LSTM) neural network model. Particulate matter, aggravated by factors such as dust, traffic, and industrialization, poses a serious threat to public health, especially in developing countries. Existing models such as the Autoregressive integrated moving average (ARIMA) have limitations in capturing nonlinear relationships and complex dynamics in environmental data. Using four years of daily data collected at the Bel Air station, this study shows that the LSTM neural network model provides more accurate forecasts with a root mean square error (RMSE) of 3.2 μg/m 3 , whereas the RMSE for ARIMA is about 6.8 μg/m 3 . The LSTM model predicts reliably up to 7 days in advance, accurately reproducing extreme values, especially during dust event outbreaks and peak travel periods. Computational analysis shows that using Graphical Processing Unit and Tensor Processing Unit processors significantly reduce the execution time, improving the model efficiency while maintaining high accuracy. Overall, these results highlight the usefulness of the LSTM network for air quality prediction and its potential for public health management in Dakar.

Keywords: PM2.5 concentrations; LSTM neural network; forecast optimization; ARIMA data assimilation; air quality prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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