Air Pollution Forecasting Using Deep Learning

Authors

  • Manal Alghieth Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Raghad Alawaji Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Safaa Husam Saleh Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Seham Alharbi Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia

DOI:

https://doi.org/10.3991/ijoe.v17i14.27369

Keywords:

Air pollution, forecasting, deep learning, LSTM

Abstract


Nowadays, air pollution is getting an extreme problem that affects the whole environment. Due to the dangerous effects of air pollution on human’s health, this study proposes an air pollution prediction system. Because of the high dust pollution in Saudi Arabia, and the fact that there is no system for predicting the percentage of air pollution in it, this study applies an air pollution prediction system to the most affected area in Saudi Arabia. This paper aims to forecast the concentrations of PM10 particles due to their dangerous effects. This study aims to align with the Saudi vision 2030 by having an ideal environment and act in an efficient way in case of a warning situation. It applies a deep learning technique, which called Long Short-Term Memory (LSTM) to predict the air pollution in Saudi Arabia and achieved exceptional results due to the low error rates that have been obtained by this study. The error rate of Mean Absolute Error (MAE) is 0.98, for Root Mean Square Error (RMSE) is 8.68 and 0.999 for R-Squared.

Cover iJOE Vol. 17 No. 14 (2021)

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Published

2021-12-14

How to Cite

Alghieth, M., Alawaji, R., Saleh, S. H., & Alharbi, S. (2021). Air Pollution Forecasting Using Deep Learning. International Journal of Online and Biomedical Engineering (iJOE), 17(14), pp. 50–64. https://doi.org/10.3991/ijoe.v17i14.27369

Issue

Vol. 17 No. 14 (2021)

Section

Papers

License

Copyright (c) 2021 Manal Alghieth, Raghad Alawaji, Safaa Husam Saleh, Seham Alharbi

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