Smart City Gnosys

Smart city article details

Title	From Data To Decision-Making: Utilizing Decision Tree For Air Quality Monitoring In Smart Urban Areas
ID_Doc	27176
Authors	Kant S.
Year	2025
Published	International Journal of Information Technology (Singapore), 17, 1
DOI	http://dx.doi.org/10.1007/s41870-024-02208-y
Abstract	Monitoring air quality is critical in the rapidly growing landscape of smart cities to ensure public health and environmental sustainability. This paper proposes an improved Decision Tree-based model for monitoring air quality in smart cities. Decision trees enable the detection of significant pollution patterns and sources by combining historical and real-time data, allowing preventive interventions to limit unfavorable consequences. Our article summarizes recent achievements after 2020, focusing on objective, methodology, findings, and accuracy. The model is partitioned into two stages. The first stage focuses on dataset preprocessing, assuring data quality and readiness for analysis. Stage 2 entails model training and testing, which results in the prediction of air quality metrics. The suggested model demonstrated its effectiveness in accurately predicting air quality with a noteworthy accuracy of 96.83%. Jupyter notebook with open-source facilities for data analysis and model building were used for the entire simulation process. With substantial insights for urban environmental management and decision-making processes, the high accuracy has shown that using this study we can improve the performance of air quality monitoring systems. According to the findings, integrating such models can significantly help develop proactive and efficient urban air quality control plans. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.
Author Keywords	Air quality; Decision tree; Machine learning

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