集体学习用于空气质量指数预测:集成梯度提升,XGBoost和堆叠与基于SHAP的可解释性
Sukhendra Singh1, Manoj Kumar2, Vishal Sengar3
1Department of Information Technology, JSS Academy of Technical Education, Noida, Noida, Uttar Pradesh, India.
Scientific reports
|February 12, 2026
概括
这项研究引入了一种用于准确空气质量预测的新型组合模型,其性能优于深度学习方法. 这种可解释的系统增强了城市空气质量管理和公共卫生倡议.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 城市空气污染是一个重大挑战,需要先进的预测和管理策略.
- 现有的机器学习和深度学习模型在动态大气条件下难以实现实时的灵活性和可扩展性.
研究的目的:
- 开发一个强大而准确的空气质量指数 (AQI) 预测模型.
- 提高空气污染预测系统的实时灵活性和可扩展性.
主要方法:
- 一个加权的投票组合模型,结合了梯度提升,CatBoost,XGBoost和LightGBM.
- 使用GridSearchCV/Optuna进行全面的数据预处理和超参数优化,并进行5倍交叉验证.
- 利用台湾空气质量数据集 (2016-2024),包括74个站点的污染物,气象数据和每小时记录.
主要成果:
- 整体模型实现了0.6553的验证平均平方误差 (MSE),明显超过了15个基线模型,包括LSTM (MSE 45.4).
- 证明了时间稳固性, Δ R2 的 Δ R2 是 -0.0037.
- SHAP分析为增强模型解释性提供了特征重要性见解.
结论:
- 拟议的可解释组合学习系统显示了改善城市空气质量管理的重大前景.
- 这些发现支持可持续的城市生活,社区健康计划和及时的空气质量干预.
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