Air Quality Index Forecasting Via Genetic Algorithm Based Improved Extreme

Project Code :TCMAPY1195

Objective

The objective of this project is to develop an innovative approach for forecasting Air Quality Index (AQI) levels by integrating a Genetic Algorithm (GA)-based Improved Extreme Learning Machine (IELM) model. Leveraging a comprehensive dataset comprising various pollutants and meteorological parameters across different cities, the aim is to enhance the accuracy of AQI prediction. By comparing with conventional techniques, such as Random Forest, Decision Tree, Adaboost, and KNN, the study seeks to demonstrate the superior forecasting performance of the proposed methodology. This research aims to contribute to advancing air quality monitoring and management systems, facilitating proactive measures to mitigate air pollution and safeguard public health.

Abstract

This study presents an innovative approach for Air Quality Index (AQI) forecasting utilizing a Genetic Algorithm (GA)-based Improved Extreme Learning Machine (IELM) model. The proposed method integrates the strengths of GA optimization with the enhanced learning capability of IELM to accurately predict AQI levels. Leveraging a comprehensive dataset encompassing various pollutants and meteorological parameters across different cities, including PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, and Xylene, the model achieves superior forecasting performance. By comparing with conventional techniques such as Random Forest, Decision Tree, AdaBoost, and KNN, our approach demonstrates its efficacy in AQI prediction. This research contributes to advancing air quality monitoring and management systems, aiding policymakers and stakeholders in implementing proactive measures to mitigate air pollution and safeguard public health.

KEYWORDS: Random Forest, Decision Tree, AdaBoost, and KNN.

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