用可解释的机器学习算法集成多种降水产品,陆地表面数据和测量站降低降水量:土耳其地中海地区的案例研究

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Enes Hisam¹, Elif Sertel², Dursun Zafer Seker²

¹Istanbul Technical University, Graduate School, Geomatics Engineering Program, Maslak, 34469, Istanbul, Turkiye.

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概括

这项研究使用机器学习和土地表面特征缩小了土耳其的格式降水数据. 结合的数据集显著提高了准确性,优于单个来源,用于更好的区域气候和水资源应用.

科学领域:

水文和气候科学水文和气候科学
地理空间数据分析.
机器学习应用机器学习应用

背景情况:

网格式降水产品对于气候研究,干旱监测和水资源管理至关重要.
现有的数据集往往缺乏地方和区域应用所需的高空间分辨率.
降低降水量对于弥合粗分辨率数据和当地需求之间的差距至关重要.

研究的目的:

在土耳其的月度格式降水数据以高空间分辨率 (0.04°) 下调.
整合地面,卫星和重新分析降水数据集与陆地表面特征.
评估各种机器学习算法和数据组合的性能,用于降低降水量.

主要方法:

采用了立体主义,随机森林,XGBoost,LightGBM和CatBoost算法.
集成多个网格式降水产品 (例如,IMERG,ERA5) 与陆地表面数据 (地形,NDVI,LST,海距离).
采用2017-2021年气象站数据验证模型,并在独立年 (2016) 进行测试.

主要成果:

与单个产品相比,组合数据集 (Comb1和Comb2) 显著提高了降水估计的准确性 (PCC> 0.79,RMSE < 39 mm,MAE < 24 mm).
实时数据组合 (Comb3) 对其组件没有提供额外的好处.
IMERG和ERA5被确定为最有影响力的降水输入;海拔和LST是陆地表面的关键特征.

结论:

集成多个数据源的机器学习模型有效地降低了土耳其的降水量.
高分辨率的降水数据可以通过结合不同的数据集和考虑土地表面特征来实现.
开发的缩小规模产品可以增强当地和区域的气候和水资源管理战略.

关键词:

立体主义的立体主义者.决策树决策树是一个决策树.可解释的机器学习网格式的降水情况.随机的森林随机的森林再次进行分析.基于卫星的降水情况.

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