International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Web-Based AI System for Forecasting and Visualizing Air Quality in Real Time

Author(s) Dr. Arifuddin Syed
Country India
Abstract Air pollution remains a critical environmental and public health challenge, necessitating timely and accurate monitoring and forecasting systems. This paper introduces a comprehensive web-based artificial intelligence (AI) system that forecasts and visualizes air quality in real time, leveraging multiple open-source data streams including pollutant concentration metrics (PM2.5, PM10, NO₂, CO, O₃) and meteorological parameters (temperature, humidity, wind speed). The system employs a Long Short-Term Memory (LSTM) neural network architecture to capture temporal dependencies in air quality data, enabling reliable predictions of Air Quality Index (AQI) values up to 48 hours ahead. Data ingestion and preprocessing pipelines are built to handle noisy and incomplete real-world datasets, utilizing techniques such as interpolation and feature scaling. The backend, developed using Flask, manages data retrieval, model inference, and API endpoints, while the frontend interface—crafted with React.js and Chart.js—provides interactive, real-time visualizations of current and forecasted air quality metrics. Validation against historical data collected from the World Air Quality Index (WAQI) and OpenAQ platforms demonstrates robust predictive performance, with a Root Mean Square Error (RMSE) of 11.9 and a coefficient of determination (R²) of 0.87. This integrated platform offers an accessible tool for environmental agencies, urban planners, and the public to monitor air pollution trends, make informed decisions, and implement timely interventions to mitigate pollution-related risks.
Keywords Air Quality Forecasting, Real-Time Visualization, Long Short-Term Memory (LSTM), Air Pollution Monitoring, Artificial Intelligence, Web-Based System, Air Quality Index (AQI), Data Preprocessing, Environmental Data, Time-Series Prediction, Flask, React.js, OpenAQ, World Air Quality Index (WAQI).
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-04
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E-ISSN 2582-2160
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