Multi-Source Precipitation Data Merging for Heavy Rainfall Events Based on Cokriging and Machine Learning Methods
Multi-Source Precipitation Data Merging for Heavy Rainfall Events Based on Cokriging and Machine Learning Methods
复制标题
基于协同克里金法和机器学习方法的强降雨事件多源降水数据融合
DOI：
10.3390/rs14071750
复制
发表时间：
2022-04
期刊：
影响因子：
5
通讯作者：
Wenlong Jing
中科院分区：
文献类型：
--
作者：
Junmin Zhang;Jianhui Xu;Xiaoai Dai;Huihua Ruan;Xulong Liu;Wenlong Jing
高时空分辨率的网格化降水数据在水文、气象、农学等领域具有重要的应用价值。气象台站的观测资料不能准确地反映大范围降水的时空分布和变化。同时，雷达降水资料在地形复杂地区精度较低，卫星降水资料空间分辨率较低。因此，采用每小时降水模型来合并来自气象站、雷达和卫星的数据;这些模型使用了五种机器学习算法（XGBoost、梯度提升决策树、随机森林（RF）、LightGBM和多元线性回归（MLR））以及CoKriging方法。在粤北地区，对2018年4次强降水事件数据进行地理数据处理，得到合并后的逐时降水数据。CoKriging方法获得了累积降水量空间分布的最佳预测，其次是基于树的机器学习（ML）算法，但值得注意的是，MLR的预测偏离了实际模式。所有机器学习方法在强降雨事件期间降水量很少的时间点表现不佳。当降水量与纬度、经度和距海岸的距离过度相关时，基于树的ML方法在某些时间点表现不佳。
Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast.
登录
查看更多内容
通过 ANN 机器学习在稀疏测量区域中使用融合数据绘制区域降水图
影响因子：
2.7
作者：
Xu Guoyin;Wang Zhongjing;Xia Ting
通讯作者：
Xia Ting
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
K. Yilmaz;R. Adler;Yudong Tian;Yang;Hong D;Harold F Pierce Be;K. K. Adler-K.;Robert F;-Hong
通讯作者：
K. Yilmaz;R. Adler;Yudong Tian;Yang;Hong D;Harold F Pierce Be;K. K. Adler-K.;Robert F;-Hong
影响因子：
3.2
作者：
E. Kalnay;A. Dalcher
通讯作者：
E. Kalnay;A. Dalcher
影响因子：
6.4
作者：
Zhang Guofeng;Tian Guanghui;Cai Daxin;Bai Rui;Jinhe Tong
通讯作者：
Zhang Guofeng;Tian Guanghui;Cai Daxin;Bai Rui;Jinhe Tong
影响因子：
3.8
作者：
Sapna Rana;J. McGregor;J. Renwick
通讯作者：
Sapna Rana;J. McGregor;J. Renwick