A Lightweight Machine Learning Framework for Urban Air Quality Prediction
Abstract
This study proposes a lightweight machine learning framework for short-term forecasting of PM2.5 and PM10 in Seoul, South Korea, using 2024 environmental data from 50 monitoring stations. This research compares a Random Forest regressor against a Linear Regression baseline. The Random Forest model outperformed the baseline model, achieving an R2 of 0.832 and 0.827 for PM2.5 and PM10, respectively. Importantly, the framework demonstrated excellent computational efficiency, with training times under a second and prediction execution by 39.67 milliseconds. These results justify deployment in cities with limited infrastructure.
Keywords: Random Forest, Linear Regression, PM2.5, PM10.
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Copyright (c) 2026 Akshovya Shrestha, Thien Phu Nguyen , Khadak Singh Bhandari, Ahmed Abdulhakim Al-Absi
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