机器学习和深度学习模型对长期雪深度逆转的比较研究
Tingyu Lu1, Rong Fan2, Lijuan Zhang2
1Heilongjiang Institute of Technology, College of Surveying and Mapping Engineering, Harbin 150050, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
准确的雪深预测对于水资源和气候研究至关重要. 机器学习模型,特别是XGBoost,在使用历史数据预测雪深方面表现出色,优于复杂的深度学习模型.
科学领域:
- 水文学的水文学
- 气候学 气候学 气候学
- 环境科学 环境科学
背景情况:
- 雪的深度对于理解雪的动态,水资源和气候至关重要.
- 准确的雪深数据对于水文建模,气候研究和寒冷地区的灾害管理至关重要.
研究的目的:
- 为了比较机器学习和深度学习模型的性能,用于日常的雪地深度检索.
- 评估气象因素,滞后雪深和物理雪参数对预测准确性的影响.
主要方法:
- 利用来自中国两个站长期的每日气象数据 (1961-2015年).
- 雪密度和白度的综合ERA5-Land再分析数据.
- 我们比较了三种机器学习 (XGBoost,随机森林,SVM) 和三种深度学习 (1D CNN,LSTM,1D CNN-LSTM) 模型.
- 设计了四个特征组合方案来评估变量重要性.
主要成果:
- 一级滞后的雪深是这两种模型类型的最关键的预测指标.
- 机器学习模型的表现优于深度学习模型,XGBoost获得了最高的精度 (R2=0.989,RMSE=1.19厘米).
- 1D CNN在深度学习模型中表现强 (R2=0.9878,RMSE=1.26厘米),但LSTM和混合模型没有提供显著的优势.
- ERA5-陆地雪的物理参数并没有大大提高预测准确度.
结论:
- 使用历史雪深和气象数据的预测框架是强大而有效的.
- 模型复杂性与预测性能没有直接相关.
- 这些发现为长期雪深重建提供了强大的解决方案,并支持冷层过程模拟和气候变化研究.
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