Intelligent Air Quality Index Prediction System Using Machine Learning and Deep Learning Techniques

Abstract

Air pollution has emerged as one of the most critical environmental challenges worldwide, significantly affecting human health, ecosystems, and climate. Accurate prediction of the Air Quality Index (AQI) plays a vital role in mitigating risks, enabling authorities to take proactive measures and helping individuals make informed decisions. This project presents an intelligent air quality prediction system that leverages machine learning and deep learning techniques to estimate AQI based on multiple environmental pollutant parameters. The system is designed using a user-friendly graphical interface developed with Python’s Tkinter library, allowing users to upload datasets, train multiple models, and perform AQI predictions seamlessly. The dataset consists of various pollutant concentrations such as PM2.5, PM10, NO, NO₂, NOx, NH₃, CO, SO₂, O₃, Benzene, Toluene, and Xylene, which are widely recognized as key contributors to air pollution. After preprocessing, including handling missing values and normalization using StandardScaler, the dataset is split into training and testing sets. The proposed system incorporates multiple regression algorithms, including Linear Regression, Random Forest Regressor, Support Vector Regression (SVR), and a Deep Neural Network model implemented using TensorFlow. Each model is trained independently, and their performance is evaluated using Mean Squared Error (MSE). The system automatically selects the best-performing model based on the lowest MSE, ensuring optimal prediction accuracy. A significant feature of the system is its ability to categorize AQI values into standard air quality levels such as Good, Satisfactory, Moderate, Poor, Very Poor, and Severe. This classification helps users easily interpret the results without requiring technical knowledge. Additionally, the system provides a graphical comparison of model performances, enabling users to understand the effectiveness of different algorithms. The integration of traditional machine learning models with deep learning techniques enhances the robustness and flexibility of the system. While simpler models like Linear Regression offer interpretability, advanced models like Random Forest and Neural Networks capture complex nonlinear relationships in the data, improving prediction accuracy. Overall, this project demonstrates a scalable, efficient, and intelligent approach to air quality prediction. It can be extended for real-time monitoring by integrating IoT sensors and deployed as a web or mobile application for broader accessibility. The  system contributes to environmental sustainability by promoting awareness and enabling data-driven decision-making for pollution control.