气候变化领域集成服务门户(CCPortal): Regional flood frequency analysis: evaluation of regions in cluster space using support vector regression

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DOI	10.1007/s11069-020-03935-8
	Regional flood frequency analysis: evaluation of regions in cluster space using support vector regression
	Haddad K.; Rahman A.
发表日期	2020
ISSN	0921030X
起始页码	489
结束页码	517
卷号	102 期号:1
英文摘要	Regional flood frequency analysis (RFFA) remains an area of active research around the world. Obtaining reliable RFFA estimates is critical for many hydrological and environmental studies. To this end, RFFA models based on multidimensional scaling (MDS) are coupled with support vector regression (SVR) to obtain improved flood quantile estimates at ungauged sites. MDS is used to form regions in catchment data space using the site characteristics and geographical distance from gauged sites. Then SVR models are applied to identify the functional relationships between flood quantiles and the catchment characteristics variables in MDS space, in particular SVR is used to capture the complexity and nonlinearity in flood generation processes that may not be easily captured in loglinear models. Four SVR models: linear, polynomial, radial basis function (RBF) and the sigmoid kernel models, are developed. The proposed approaches are applied to 202 catchments in the States of New South Wales and Victoria, Australia. The leave-one-out validation procedure is used to evaluate the performance of the proposed models. Results of the proposed models are compared with Bayesian generalised least squares regression which is also developed in MDS space. The results indicate that the MDS-RBF-based SVR model provides more consistent flood quantile estimates than the competing SVR models. The RBF SVR approach also demonstrates better generalisation ability than the competing SVR models examined. The MDS-based SVR RBF kernel generally shows the best performance among all the models in terms of prediction accuracy. Overall, the main strengths of the SVR are that it captures efficiently the nonlinearity between the dependent and predictor variables and when coupled with MDS for delineation of regions, the prediction ability of the model increases along with a reduction in bias and relative error. © 2020, Springer Nature B.V.
关键词	Multidimensional scaling Nonlinear model Regional frequency analysis Regression SVR Ungauged basin
英文关键词	accuracy assessment; catchment; complexity; flood forecasting; flood frequency; flow modeling; model test; quantitative analysis; regression analysis; reliability analysis; support vector machine; Australia; New South Wales; Victoria [Australia]
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/205712
作者单位	Cumberland Council, 16 Memorial Avenue, P.O. Box 42, Merrylands, NSW 2160, Australia; School of Computing, Engineering and Mathematics, Western Sydney University, Building XB, Kingswood, Locked Bag 1797, Penrith, NSW 2751, Australia
推荐引用方式 GB/T 7714	Haddad K.,Rahman A.. Regional flood frequency analysis: evaluation of regions in cluster space using support vector regression[J],2020,102(1).
APA	Haddad K.,&Rahman A..(2020).Regional flood frequency analysis: evaluation of regions in cluster space using support vector regression.Natural Hazards,102(1).
MLA	Haddad K.,et al."Regional flood frequency analysis: evaluation of regions in cluster space using support vector regression".Natural Hazards 102.1(2020).