Title:Air Pollution Monitoring and Prediction System
Authors:Ansh Jaiswal, Anmol Soni, Aaysha Khan ,Shruti Lashkari, Ashish Anjana
Published in: Volume 3 Issue 1 Jan June 2026, Page No.434-442
DOI: 10.63844/IJAITR.v3.i1.2026.434-442 cite
Keywords:Spring Boot, Spring Security, React,
Machine Learning, AQI, Data Analysis
Abstract: Air pollution has been a growing concern
in recent years, making it essential to measure
and analyze air quality. Previous research has
leveraged machine learning algorithms to forecast the
Air Quality Index (AQI) for specific locations. While
these models have achieved reliable results, they still
face challenges such as low accuracy and insufficient
data analysis. In this paper, we propose a web-based
air quality prediction system using Java technologies,
including Spring Boot, JSP, and Servlets. The system
integrates machine learning models such as Random
Forest, XGBoost, and Neural Networks to predict AQI
for various cities in India. The backend is developed
using Spring Boot for efficient data processing and
API management, while the frontend leverages React
to display real-time AQI predictions. The system39;s
performance is evaluated using Root Mean Square
Error (RMSE) and the Coefficient of Determination
(R²) to ensure accurate predictions. Additionally, data
analysis techniques are applied to enhance model
accuracy. This paper demonstrates how Java-based
technologies can be effectively utilized to develop a
scalable and robust air quality prediction platform.
Download PDF |
.png)
.