Air Quality Index Prediction by Using Machine and Deep Learning

Project Code :TCMAPY1050

Objective

The objective of this project is to develop accurate Air Quality Index (AQI) prediction system by leveraging both machine learning and deep learning techniques. We aim to collect historical air quality data, meteorological information, and other relevant factors to train predictive models. Through this research, we intend to enhance our understanding of the complex relationship between various variables and air quality, ultimately enabling us to forecast AQI values with high precision. This project seeks to provide valuable insights for proactive air quality management and public health measures.

Abstract

This study presents a novel approach to predict Air Quality Index (AQI) using both machine learning and deep learning techniques. By leveraging historical air quality data, meteorological variables, and pollutant concentrations, our model aims to provide accurate real-time AQI forecasts. The machine learning component utilizes regression algorithms for baseline prediction, while the deep learning component employs convolutional neural networks (CNNs) for feature extraction and forecasting. Through comprehensive evaluation and validation, our hybrid model demonstrates promising results, enhancing our ability to forecast AQI and consequently support informed decision-making for air quality management and public health.

Keywords: - CNN, Random Forest, Decision tree and dataset.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.