Utilizing Machine Learning for Air Quality Prediction and Management in Urban Areas
Abstract
Air pollution poses severe health and environmental challenges in densely populated urban areas. This paper introduces a machine learning framework to forecast air quality indices and identify pollution trends based on meteorological and traffic data. The model allows for real-time monitoring and can inform proactive public health measures to minimize exposure in high-risk areas. Our findings indicate that machine learning can enhance urban air quality management by accurately predicting pollution spikes, helping policymakers implement timely interventions, and contributing to cleaner, healthier cities.
References
Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.
Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.
Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).
Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).
Boppiniti, S. T. (2023). Data Ethics in AI: Addressing Challenges in Machine Learning and Data Governance for Responsible Data Science. International Scientific Journal for Research, 5(5).
Boppiniti, S. T. (2020). Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets. International Journal of Creative Research In Computer Technology and Design, 2(2).
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., Perumal, A. P., & Gopal, S. K. (2023). Beyond the Bin: Machine Learning-Driven Waste Management for a Sustainable Future. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 11(1), 16-27.
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
