用机器学习和值温度方法对波兰的降水类型进行分类.
Quoc Bao Pham1, Ewa Łupikasza2, Małarzewski Łukasz1
1Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200, Sosnowiec, Poland.
Scientific reports
|November 25, 2023
概括
准确地分类降雨类型,如雨和雪,对于水文学至关重要. 机器学习,特别是随机森林,被证明是最有效的,在区分降雨和降雪方面取得了很高的准确性.
科学领域:
- 水文和气象学.
- 气候学和大气科学 气候学和大气科学
背景情况:
- 降水阶段 (雨,雪,冰雪) 显著影响水文和表面下水.
- 来自气象站的降水阶段数据的有限可用性和不完整性构成了挑战.
研究的目的:
- 用40个波兰气象站 (1966-2020) 的数据将降水分为降雨和降雪.
- 将机器学习 (随机森林) 的准确性与基于温度的降水分类方法进行比较.
主要方法:
- 利用了波兰40个气象站 (1966-2020) 的数据.
- 采用了三种降雨/降雪分类方法:随机森林 (RF),每日平均气温值和每日湿球值温度.
- 研究了水蒸气压力和平均湿球温度对射频模型准确性的影响.
主要成果:
- 随机森林 (RF) 方法实现了最高的分类准确度 (0.90-1.00).
- 基于温度的方法 (平均湿球和平均空气温度) 显示出类似的准确性 (0.86-1.00).
- 为每个站点确定了优化的值温度;水蒸气压提高了射频精度,同时消除了平均湿球温度提高了射频精度.
结论:
- 与基于温度的方法相比,随机森林模型在区分降雨和降雪方面提供了更高的准确性.
- 水蒸气压力是降水分类模型中的一个重要因素.
- 需要进一步的研究来了解分类有效性的特定站的变化.
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