基于可解释机器学习的城市 AQI 估计
Siyuan Wang1, Ying Ren1, Bisheng Xia2
1School of Mathematics and Computer Science, Yan'an University, Yan'an, 716000, China.
Environmental science and pollution research international
|August 14, 2023
概括
机器学习使用污染物数据准确预测空气质量指数 (AQI). XGBoost模型表现出卓越的性能,确定PM2.5和PM10是空气污染的主要驱动因素.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 大气化学 大气化学
背景情况:
- 空气污染是一个重大的全球性挑战,影响人类健康和生活质量.
- 准确的空气质量预测对于有效的污染控制和减缓战略至关重要.
- 空气质量指数 (AQI) 提供了一个标准化的衡量标准,用于传达空气质量水平.
研究的目的:
- 开发和评估机器学习模型,用于估计中国石家庄的空气质量指数 (AQI).
- 在 AQI 预测中比较 eXtreme Gradient Boosting (XGBoost),Light Gradient Boosting Machine (LightGBM) 和 Random Forest (RF) 模型的性能.
- 使用模型解释性技术识别影响AQI变化的关键因素.
主要方法:
- 使用机器学习算法,包括XGBoost,LightGBM和RF用于AQI估计.
- 使用污染物度和气象因素作为模型的输入变量.
- 应用SHAP (夏普利添加式解释) 来解释模型以确定特征的重要性.
主要成果:
- XGBoost模型实现了最高的预测准确度,R2为0.929,超过了RF和LightGBM.
- 确定PM2.5和PM10是导致AQI变化的主要因素.
- 与颗粒物相比,气象因素对AQI的影响不那么大.
结论:
- 机器学习方法,特别是XGBoost,对于准确的空气质量预测是有效的.
- 了解PM2.5和PM10等特定污染物的影响对于有针对性的空气污染管理至关重要.
- 开发的模型证明了在不同中国城市的普遍性和良好表现.
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