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Accurate seasonal air quality prediction is crucial for effective environmental management and public health. This study introduces a novel approach that integrates Polynomial Regression with Bayesian Optimization for forecasting air quality indices (AQI). Traditional ensemble models such as Gradient Boosting, Random Forest, XGBoost, LightGBM and CatBoost, while effective, often face challenges like overfitting, high computational demands and complex hyperparameter tuning. Our approach addresses these issues by using Polynomial Regression to capture non-linear relationships and Bayesian Optimization to streamline hyperparameter tuning, enhancing model efficiency and interpretability. The proposed model outperforms conventional methods based on Mean Squared Error (MSE). Seasonal pollutant analysis also highlights unique patterns in air quality changes, providing deeper insights for better environmental decision-making. Future research will focus on integrating advanced techniques and more variables to further refine and broaden the model’s applicability.

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Intelligent Seasonal Air Quality Prediction with Machine Learning Models: Enhancing Performance Through Polynomial Regression and Bayesian Optimization

  • 会议论文

  • 发表日期：

    2025年

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  • Sneh Padaliya,
  • Swati Saxena,
  • Aloknath De

摘要

Accurate seasonal air quality prediction is crucial for effective environmental management and public health. This study introduces a novel approach that integrates Polynomial Regression with Bayesian Optimization for forecasting air quality indices (AQI). Traditional ensemble models such as Gradient Boosting, Random Forest, XGBoost, LightGBM and CatBoost, while effective, often face challenges like overfitting, high computational demands and complex hyperparameter tuning. Our approach addresses these issues by using Polynomial Regression to capture non-linear relationships and Bayesian Optimization to streamline hyperparameter tuning, enhancing model efficiency and interpretability. The proposed model outperforms conventional methods based on Mean Squared Error (MSE). Seasonal pollutant analysis also highlights unique patterns in air quality changes, providing deeper insights for better environmental decision-making. Future research will focus on integrating advanced techniques and more variables to further refine and broaden the model’s applicability.