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DOI	10.2166/nh.2021.082
	Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks
	Filipova, Valeriya; Hammond, Anthony; Leedal, David; Lamb, Rob
通讯作者	Filipova, V (corresponding author), JBA Risk Management, Skipton, England.
来源期刊	HYDROLOGY RESEARCH
ISSN	1998-9563
EISSN	2224-7955
出版年	2021-12
英文摘要	In this study, we utilise Artificial Neural Network (ANN) models to estimate the 100- and 1500-year return levels for around 900,000 ungauged catchments in the contiguous USA. The models were trained and validated using 4,079 gauges and several selected catchment descriptors out of a total of 25 available. The study area was split into 15 regions, which represent major watersheds. ANN models were developed for each region and evaluated by calculating several performance metrics such as root-mean-squared error (RMSE), coefficient of determination ( R (2) ) and absolute percent error. The availability of a large dataset of gauges made it possible to test different model architectures and assess the regional performance of the models. The results indicate that ANN models with only one hidden layer are sufficient to describe the relationship between flood quantiles and catchment descriptors. The regional performance depends on climate type as models perform worse in arid and humid continental climates. Overall, the study suggests that ANN models are particularly applicable for predicting ungauged flood quantiles across a large geographic area. The paper presents recommendations about future application of ANN in regional flood frequency analysis.
英文关键词	ANN models flood frequency analysis machine learning ungauged basins
类型	Article ; Early Access
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000731400500001
WOS关键词	FREQUENCY-ANALYSIS ; WORLD MAP
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374683
作者单位	[Filipova, Valeriya; Hammond, Anthony; Leedal, David] JBA Risk Management, Skipton, England; [Lamb, Rob] JBA Trust, Skipton, England; [Lamb, Rob] Lancaster Environm Ctr, Lancaster, England
推荐引用方式 GB/T 7714	Filipova, Valeriya,Hammond, Anthony,Leedal, David,et al. Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks[J],2021.
APA	Filipova, Valeriya,Hammond, Anthony,Leedal, David,&Lamb, Rob.(2021).Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks.HYDROLOGY RESEARCH.
MLA	Filipova, Valeriya,et al."Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks".HYDROLOGY RESEARCH (2021).