可解释的机器学习用于使用XGBoost-SHAP进行吹雪探测和气象特征评估的预测建模
Feng Wang1,2, Xinrang Wang2, Sai Li3
1School of Civil Engineering, Beijing Jiaotong University, Beijing, China.
PloS one
|March 28, 2025
概括
这项研究使用 eXtreme Gradient Boosting (XGBoost) 精确预测吹雪事件. 影响这些事件的关键因素包括风速,空气温度和湿度,提高了气象模型的解释性.
科学领域:
- 气象学和大气科学 气象学和大气科学
- 环境科学中的人工智能
背景情况:
- 精确预测吹雪事件对于改善雪过程模型至关重要.
- 传统的预测方法往往缺乏解释性,阻碍了有效的决策.
研究的目的:
- 开发一种可解释的模型,用机器学习来检测吹雪事件.
- 识别和量化气象因素对吹雪发生的影响.
主要方法:
- 使用极端梯度提升 (XGBoost) 进行事件检测.
- 在模型性能评估中使用5倍交叉验证.
- 应用SHAP (夏普利增量解释) 和部分依赖图 (PDP) 进行因子分析.
主要成果:
- XGBoost 在非吹雪事件 (>0.94) 中获得了高精度,在吹雪事件 (0.77-0.80) 中获得了良好的性能.
- 最大风速,平均风速,空气温度和相对湿度被确定为关键预测因素.
- 吹雪的概率随着最大风速的增加而增加,并且在-3°C以上的空气温度下降;相对湿度超过60%会稳定发生.
结论:
- XGBoost提供了一种可解释和准确的方法来检测吹雪事件.
- 可解释的AI技术提高了对吹雪动态的理解.
- 结果支持气象应用和雪过程建模中的数据驱动决策.
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