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一个基于自动编码器的雪旱指数
Sinan Rasiya Koya1, Kanak Kanti Kar1, Shivendra Srivastava1
1Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, USA.
Scientific reports
|November 24, 2023
概括
我们开发了雪旱响应指数 (SnoDRI) 来检测和测量雪旱. 这个新指标使用机器学习来分析雪数据,改善依赖雪的地区的水资源管理.
科学领域:
- 水文和水资源水文与水资源
- 气候科学 气候科学
- 机器学习应用 机器学习应用
背景情况:
- 融雪显著影响全球水文,影响地下水透和地表下水.
- 雪地干旱是由于积雪减少造成的,对水源构成重大风险,特别是在像美国西部这样的地区.
- 有效地检测雪旱的时间和严重程度对于水资源管理至关重要.
研究的目的:
- 引入一个新的指标,即雪旱响应指数 (SnoDRI),用于识别和量化雪旱事件.
- 为了评估SnoDRI的疗效,使用来自美国太平洋地区的历史数据.
- 评估各种与雪有关的变量在雪干旱检测中的重要性.
主要方法:
- 使用自我监督学习 (自动编码器) 和相互信息的组合开发了SnoDRI.
- 在SnoDRI模型中,用于特征提取和变量重要性评估的运用随机森林.
- 利用了NLDAS-2重复分析数据,涵盖1981-2021年美国太平洋地区的数据.
主要成果:
- 在SnoDRI有效地识别和量化雪旱的发生.
- 变量重要性分析揭示了导致雪旱检测的关键因素.
- 该指数的解释表明,与历史上的雪旱事件有很强的巧合.
结论:
- 斯诺德里 (SnoDRI) 提供了一种可靠的方法来监测雪旱状况.
- 开发的指数可以增强雪占主导地位的盆地水资源管理策略.
- 机器学习方法显示出对推动雪地水文研究的重大前景.
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