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JCSIS

Feature Selection Optimization for Air Quality Prediction Using Machine Learning in Urban Environments

Nader BehdadWeiguo GeeWang Zhang
Volume 4

Abstract

Accurate prediction of air quality is essential for public health management and environmental sustainability in urban areas. This study proposes an optimized machine learning framework for air quality prediction, emphasizing feature selection to enhance model accuracy and computational efficiency. Multiple urban datasets comprising meteorological parameters, vehicular emissions, industrial activity, and pollutant concentrations are preprocessed and subjected to advanced feature selection techniques, including Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and metaheuristic-based methods such as Genetic Algorithms (GA). These selected features are then used to train machine learning models including Random Forests, Support Vector Machines (SVM), and Gradient Boosting. Results indicate a significant improvement in prediction accuracy and processing time, supporting proactive environmental management and policy development in smart city contexts.

Keywords

Air quality prediction, feature selection, machine learning, urban environments, smart cities, environmental monitoring

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