Air pollution forecasting offers crucial support for pollution control and regulation. However, the non-linearity, volatility, and complexity of air pollution data pose significant challenges to accurate prediction. To address these challenges, this study develops a multi-variate combined forecasting framework based on feature selection methods, linear and nonlinear forecasting models, and intelligent optimization algorithms. Firstly, the framework analyzes the impact of each subset after feature selection on model performance. Secondly, multiple benchmark models based on different feature subsets and various prediction models are constructed as comparison models to further test the performance of the proposed prediction framework. Finally, two real-world datasets, statistical hypothesis testing, and several evaluation metrics are used to validate the prediction performance of the proposed framework. The Root Mean Square Error (RMSE) of the proposed model on the Site 1 dataset is 1.9762, representing an improvement of approximately 4 % compared to the best-performing benchmark model. Statistical testing further confirms that the proposed model significantly outperforms the benchmark model. The proposed combined multi-variate forecasting framework provides a heuristic framework for building combined prediction models with high prediction accuracy and robustness.

中文翻译：

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一种基于特征选择和深度学习模型的新型空气污染组合多变量预测框架


空气污染预报为污染控制和监管提供了重要支持。然而，空气污染数据的非线性、波动性和复杂性对准确预测构成了重大挑战。为了应对这些挑战，本研究开发了一个基于特征选择方法、线性和非线性预测模型以及智能优化算法的多变量组合预测框架。首先，框架分析了特征选择后每个子集对模型性能的影响;其次，构建了基于不同特征子集的多个基准模型和各种预测模型作为对比模型，以进一步测试所提预测框架的性能。最后，使用两个真实世界数据集、统计假设检验和几个评估指标来验证所提出的框架的预测性能。在 Site 1 数据集上，所提出的模型的均方根误差 （RMSE） 为 1.9762，与性能最佳的基准模型相比，提高了约 4%。统计测试进一步证实，所提出的模型明显优于基准模型。所提出的组合多变量预测框架为构建具有高预测精度和鲁棒性的组合预测模型提供了一个启发式框架。