Machine Learning-Based System for Weather Prediction and Air Quality Index Estimation
DOI:
https://doi.org/10.5281/zenodo.11084950Keywords:
Air Quality Index, Django, Random Forest, Weather Prediction, CloudFront, ReactJs, WebviewAbstract
This paper presents a study on "Machine Learning for Weather Prediction and Air Quality Index Estimation," aimed at enhancing weather forecasting and air quality monitoring. Integrating historical weather data with real-time atmospheric measurements from the OpenWeather API, the study utilizes the Random Forest Machine Learning algorithm to construct predictive models. Backend operations are managed by a Django application on AWS EC2, supported by Nginx as a reverse proxy. The frontend, a ReactJS-based web app hosted on AWS S3 and distributed via CloudFront, offers an intuitive interface. Additionally, a dedicated mobile app extends the system's reach, delivering real-time updates on weather conditions and air quality. This comprehensive approach empowers users with precise insights for informed decision-making and environmental awareness.
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Copyright (c) 2024 Adnan Mohammed, S. Roshan Zameer, Umar Chowdhry, Dr. Ashok Kumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Research Articles in 'International Journal of Engineering and Management Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
