气候变化领域集成服务门户(CCPortal): Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach

CCPortal

DOI	10.1016/j.jhydrol.2021.125969
	Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach
	Zhang, Ling; Li, Xin; Zheng, Donghai; Zhang, Kun; Ma, Qimin; Zhao, Yanbo; Ge, Yingchun
通讯作者	Zhang, L (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China.
发表日期	2021
ISSN	0022-1694
EISSN	1879-2707
卷号	594
英文摘要	This study proposed a novel double machine learning (DML) approach to merge multiple satellite-based precipitation products (SPPs) and gauge observations, and tested its reliability and validity over the Chinese mainland. The DML approach was mainly developed based on the classification model of random forest (RF) in combination with the regression models of the machine learning (ML) algorithms including RF, artificial neural network (ANN), support vector machine (SVM) and extreme learning machine (ELM). This led to four DML algorithms, i.e., RF-RF, RF-ANN, RF-SVM, and RF-LM. The performance of the DML algorithms were compared to the single machine learning (SML) algorithms developed based solely on the regression models of RF, ANN, SVM, and ELM, and to the liner merging methods including the inverse error variance weighting, the one-outlier-removed average, and the optimized weight average. In total, we produced twelve precipitation products including four of the DML algorithms, four of the SML algorithms, three of the liner merging methods, and another one generated via the gauge-only interpolation. The precipitation observations at 697 gauges were spatially and randomly divided into two parts (i.e., 70% and 30%), one was used for the training of the ML algorithms or for the interpolation, while the other for the performance evaluations. Results indicate that the DML algorithms outperform the other merging methods, the gauge-only interpolation, and the original SPPs over the Chinese mainland. The median Kling-Gupta efficiency (KGE) ranges 0.67-0.71 for the merged products of DML, which are obviously higher than the original SPPs (0.31-0.54), the linear merged product (0.54-0.55), gauge-only interpolated product (0.62), and the SML-based products (0.47-0.65). The DML-based products also exhibit better performances than the other products in detecting precipitation events with the threshold of 1 mm/day, and outperform the original SPPs regardless of the precipitation thresholds. Further analyses imply that: (i) the DML-based products could outperform the original SPPs even with a small training dataset size; (ii) the superiority of the DML approach to SML is mainly due to that the former can better capture the temporal dynamics of precipitation; (iii) the added values of the merged products of DML relative to the original SPPs and the gauge-only product vary with the sizes of the training dataset; and (iv) the ensemble of the DML algorithms could not further improve the accuracy of the precipitation estimates. This study not only provided an effective and robust tool for the fusion of multiple SPPs and gauge observations, but also, for the first time, compared the performance of various ML algorithms in merging satellite and gauge-based precipitation.
关键词	ARTIFICIAL NEURAL-NETWORKS INTERPOLATION TECHNIQUES RAINFALL DATA REGRESSION PERFORMANCE ALGORITHM SYSTEM REGION ERROR BASIN
英文关键词	Precipitation merging; Satellite-based precipitation product; Machine learning; Gauge density; Chinese mainland
语种	英语
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:000641589600072
来源期刊	JOURNAL OF HYDROLOGY
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254240
作者单位	[Zhang, Ling; Zhao, Yanbo; Ge, Yingchun] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China; [Li, Xin; Zheng, Donghai; Zhang, Kun] Chinese Acad Sci, Natl Tibetan Plateau Data Ctr, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100101, Peoples R China; [Li, Xin] CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China; [Ma, Qimin] Chengdu Univ Informat Technol, Coll Resources & Environm, Chengdu 610225, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Ling,Li, Xin,Zheng, Donghai,et al. Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach[J]. 中国科学院西北生态环境资源研究院,2021,594.
APA	Zhang, Ling.,Li, Xin.,Zheng, Donghai.,Zhang, Kun.,Ma, Qimin.,...&Ge, Yingchun.(2021).Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach.JOURNAL OF HYDROLOGY,594.
MLA	Zhang, Ling,et al."Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach".JOURNAL OF HYDROLOGY 594(2021).