PREDICTING AIR POLLUTION LEVEL IN SPECIFIC CITY

The project “Predicting Air Pollution Levels in a Specific City” illustrates the app of ML to tackle environmental issues. The model predicts future pollution levels by examining past and present air quality data. This project aids in identifying key locations where air quality becomes dangerous and comprehending trends in air pollution. Future work includes Several cutting-edge elements could be added to the air pollution level forecasting system to increase its accuracy utility and impact. One crucial development is the addition of real-time IoT sensors which offer consistent and more precise information on air quality. As a outcome the platform can produce predictions that are most accurate and up to date.

  1. WHO Air Quality Guidelines: Global Update Geneva: WHO Press 2005.  2. Central Pollution Control Board and the Government of India National Air Quality Monitoring Program.  3. A U. In S. EPA is the Environmental Protection Agency. The Air Quality Index (AQI) Fundamentals.  4. Bishop Charles. M. (2006). Pattern recognition and machine learning. The Springer. 5. Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. 6. Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, 2019. 7. Box, G. E. P., Jenkins, G. M., and Reinsel, G. C., Time Series Analysis: Forecasting and Control, Wiley, 2015.  8. Kumar and associates. Using Machine Learning Methods to Predict Air Quality. . Software and Environmental Modeling Elsevier.  9. Open Air Quality Data Repository OpenAQ Community and OpenAQ Platform.