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<p>The prediction and classification of the air quality index (AQI) are a primary concern. Still, due to the involvement of many parameters affecting AQI pre- dictions, it becomes time-consuming and monotonous work. The prediction and classification of the AQI with utmost accuracy is a pivotal tool for evaluating and monitoring the level of air pollution in a given area, facilitating public awareness and policy making in safeguarding human health and the surroundings. In this study, to improve the performance of air quality index classification and prediction, machine learning techniques based on support vector regression, and for prediction, the classic time-series analysis based on seasonal autoregressive integrated moving average (SARIMA) was employed. The forecasted AQI value for the next 25&#xa0;days lies in a 97.3% confidence interval zone. The experimental results of the proposed method are evaluated and validated for performance and quality analysis on AQI data based on accuracy, precision, recall, and F1 score. The experimental results achieved 97% accuracy, compared to 94.1% with the Auto-regressive integrated moving average (ARIMA)-based technique, 91% precision, 94% recall, and 92% F1 score, demonstrating the effectiveness of the proposed technique for classifying and predicting the air quality index.</p>

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SVR machine learning and SARIMA-based air quality index classification and forecasting system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月26日

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  • Nilesh Bhaskarrao Bahadure,
  • Sudhanshu Gonge,
  • Deepak Parashar,
  • Bhoomi Shah,
  • Prasenjeet Damodar Patil,
  • M. Renugadevi,
  • Nagrajan Raju

摘要

The prediction and classification of the air quality index (AQI) are a primary concern. Still, due to the involvement of many parameters affecting AQI pre- dictions, it becomes time-consuming and monotonous work. The prediction and classification of the AQI with utmost accuracy is a pivotal tool for evaluating and monitoring the level of air pollution in a given area, facilitating public awareness and policy making in safeguarding human health and the surroundings. In this study, to improve the performance of air quality index classification and prediction, machine learning techniques based on support vector regression, and for prediction, the classic time-series analysis based on seasonal autoregressive integrated moving average (SARIMA) was employed. The forecasted AQI value for the next 25 days lies in a 97.3% confidence interval zone. The experimental results of the proposed method are evaluated and validated for performance and quality analysis on AQI data based on accuracy, precision, recall, and F1 score. The experimental results achieved 97% accuracy, compared to 94.1% with the Auto-regressive integrated moving average (ARIMA)-based technique, 91% precision, 94% recall, and 92% F1 score, demonstrating the effectiveness of the proposed technique for classifying and predicting the air quality index.