AIRSENSEML: INTELLIGENT MACHINE LEARNING FRAMEWORK FOR URBAN AIR POLLUTION PREDICTION

Abstract

Air pollution has become one of the most significant environmental and health challenges in urban areas worldwide. Predicting pollution levels accurately is essential for developing effective mitigation strategies, enforcing environmental regulations, and safeguarding public health. However, traditional air quality forecasting models often struggle to capture the complex, nonlinear relationships among diverse environmental factors such as temperature, humidity, wind speed, and particulate matter concentration. This paper introduces AirSenseML, an intelligent machine learning framework designed for real-time air pollution prediction in urban environments. The proposed system integrates data from various sources, including environmental sensors, meteorological data, and satellite feeds, to train advanced algorithms such as Random Forest (RF), Gradient Boosting (GBM), and Long Short-Term Memory (LSTM) networks. AirSenseML emphasizes data preprocessing, feature correlation analysis, and ensemble learning to achieve high accuracy and interpretability. Experimental results demonstrate that the proposed framework significantly outperforms traditional regressionbased and statistical models, achieving an accuracy improvement of up to 25%. By delivering actionable insights for policymakers and environmental agencies, AirSenseML contributes to building smart, sustainable, and healthy urban ecosystems.