Back to all available

Air pollution forecasting with machine learning in post-disaster zones: A case study of PM2.5 in Turkey’s earthquake regions

artificial-intelligence
computer-vision
data-mining-and-machine-learning
visual-analytics
neural-networks

Abstract

Air pollution continues to be a critical challenge for the environment and public health, particularly in regions that have been affected by natural disasters. This study aims to predict PM2.5 air pollution levels in the earthquake-affected provinces of Turkey following the February 6, 2023, earthquakes, using machine learning and deep learning techniques. Data were collected from air quality monitoring stations in several provinces, including Adana, Gaziantep, Hatay, Osmaniye, and Kahramanmaras. The study examines the relationship between PM 2.5 and other air pollutants (PM 10, SO2, CO, NO2, NOX, and NO) and meteorological parameters such as air temperature, humidity at the air, wind speed during the day, and air pressure. Different prediction models, including neural networks (FBNN, NARX) and deep learning methods (XGBoost, LGBM, RDF), are used, and their effectiveness in predicting PM 2.5 concentrations is compared. The results show that the Nonlinear Autoregressive Network with External Input (NARX) model, trained with the Levenberg-Marquardt algorithm, outperformed the other methods with an R² value of 0.92460 and an RMSE of 13.1873 μg/m³.
Ask to review this manuscript

Notes for potential reviewers

  • Volunteering is not a guarantee that you will be asked to review. There are many reasons: reviewers must be qualified, there should be no conflicts of interest, a minimum of two reviewers have already accepted an invitation, etc.
  • This is NOT OPEN peer review. The review is single-blind, and all recommendations are sent privately to the Academic Editor handling the manuscript. All reviews are published and reviewers can choose to sign their reviews.
  • What happens after volunteering? It may be a few days before you receive an invitation to review with further instructions. You will need to accept the invitation to then become an official referee for the manuscript. If you do not receive an invitation it is for one of many possible reasons as noted above.
  • PeerJ Computer Science does not judge submissions based on subjective measures such as novelty, impact or degree of advance. Effectively, reviewers are asked to comment on whether or not the submission is scientifically and technically sound and therefore deserves to join the scientific literature. Our Peer Review criteria can be found on the "Editorial Criteria" page - reviewers are specifically asked to comment on 3 broad areas: "Basic Reporting", "Experimental Design" and "Validity of the Findings".
  • Reviewers are expected to comment in a timely, professional, and constructive manner.
  • Until the article is published, reviewers must regard all information relating to the submission as strictly confidential.
  • When submitting a review, reviewers are given the option to "sign" their review (i.e. to associate their name with their comments). Otherwise, all review comments remain anonymous.
  • All reviews of published articles are published. This includes manuscript files, peer review comments, author rebuttals and revised materials.
  • Each time a decision is made by the Academic Editor, each reviewer will receive a copy of the Decision Letter (which will include the comments of all reviewers).

If you have any questions about submitting your review, please email us at [email protected].