Post-Earthquake Air Quality Prediction: A Machine Learning-Based Approach in Kahramanmaraş and Hatay Provinces

Abstract

Air pollutants significantly impact human health, from mild symptoms to premature death. The devastating earthquake in Kahramanmaraş on February 6, 2023, increased air pollution risks due to debris and heating activities. Estimating post-earthquake air quality is crucial for controlling pollutants like PM10and SO2, which affect respiratory health. This study applies machine learning methods to assess air quality using pollutant concentration data from Kahramanmaraş, Hatay, and Adana, sourced from the Ministry of Environment’s Air Monitoring Stations. Five machine learning algorithms were evaluated; the Decision Tree achieved the highest accuracy (0.9987 and 0.9910), while Logistic Regression performed the worst. Results indicate that the proposed model can effectively predict air quality and support authorities in mitigating pollution-related risks.

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