气候变化领域集成服务门户(CCPortal): Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US

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DOI	10.1088/1748-9326/aba927
	Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
	Konapala G.; Kao S.-C.; Painter S.L.; Lu D.
发表日期	2020
ISSN	17489318
卷号	15 期号:10
英文摘要	Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide future hydroclimate projections where climates and land use patterns are outside the range of training data. Here we test hybrid models built by integrating PB model outputs with an ML algorithm known as long short-term memory (LSTM) network on their ability to simulate streamflow in 531 catchments representing diverse conditions across the Conterminous United States. Model performance of hybrid models as measured by Nash–Sutcliffe efficiency (NSE) improved relative to standalone PB and LSTM models. More importantly, hybrid models provide highest improvement in catchments where PB models fail completely (i.e. NSE < 0). However, all models performed poorly in catchments with extended low flow periods, suggesting need for additional research. © 2020 The Author(s). Published by IOP Publishing Ltd
英文关键词	Long short-term memory (LSTM) network; Machine learning (ML); Process-based hydrologic models; Sacramento soil moisture accounting model (SAC)
语种	英语
scopus关键词	Catchments; Land use; Machine learning; Runoff; Stream flow; Hydrologic models; Land use pattern; Model outputs; Model performance; Physical process; Streamflow modeling; Streamflow simulations; Structural errors; Long short-term memory; algorithm; catchment; climate prediction; land use; low flow; machine learning; streamflow; training; United States
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153647
作者单位	Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States; Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States
推荐引用方式 GB/T 7714	Konapala G.,Kao S.-C.,Painter S.L.,et al. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US[J],2020,15(10).
APA	Konapala G.,Kao S.-C.,Painter S.L.,&Lu D..(2020).Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US.Environmental Research Letters,15(10).
MLA	Konapala G.,et al."Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US".Environmental Research Letters 15.10(2020).