An Intelligent Prediction Model for Air Quality Monitoring Using GA-ELM

Abstract

Abstract— Air pollution poses one of the most serious environmental threats to public health and sustainable urban living. Accurate and timely forecasting of the Air Quality Index (AQI) can support policymakers, environmental agencies, and citizens in taking proactive measures to mitigate pollution effects. This project presents an optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality. The ELM component provides a high-speed, single-hidden layer feedforward neural network capable of mapping complex non-linear relationships between pollutants. To overcome the traditional challenge of random weight initialization in ELMs, a Genetic Algorithm (GA) is implemented to "evolve" and select the most optimal input weights and biases. This hybrid approach ensures that the model captures intricate correlations among multiple pollutants and meteorological variables with significantly higher computational efficiency than iterative deep learning models. A comprehensive dataset containing historical pollutant concentrations (PM2.5, PM10, NO₂, SO₂, CO, NH₃, O₃) and meteorological parameters (temperature, humidity, wind speed, etc.) is collected and preprocessed to remove noise, handle missing values, and normalize features using Min-Max scaling. The hybrid GA-ELM model is trained and evaluated using key performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Experimental results demonstrate that the proposed architecture achieves superior prediction speed and stability compared to traditional regression models and standalone neural networks. In addition to the model, the system includes an interactive Flask-based web dashboard for real-time AQI visualization and forecasting. The dashboard provides 1–12 hour ahead predictions, dynamic charts, and color-coded AQI indicators based on national standards for easy interpretation.Overall, the developed system demonstrates a robust, scalable, and practical solution for short-term air quality prediction. .