DOI: 10.1002/2016MS000830
Scopus记录号: 2-s2.0-85019706757
论文题名: Fully nonlinear statistical and machine-learning approaches for hydrological frequency estimation at ungauged sites
作者: Ouali D ; , Chebana F ; , Ouarda T ; B ; M ; J
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2017
卷: 9, 期: 2 起始页码: 1292
结束页码: 1306
语种: 英语
英文关键词: Artificial intelligence
; Catchments
; Complex networks
; Frequency estimation
; Learning systems
; Neural networks
; Nonlinear systems
; Hydrological modeling
; Hydrological process
; Model comparison
; Quantile estimation
; Regional frequency analysis
; Nonlinear analysis
; catchment
; complexity
; correlation
; estimation method
; frequency analysis
; hydrological modeling
; machine learning
; model test
; numerical model
; North America
英文摘要: The high complexity of hydrological systems has long been recognized. Despite the increasing number of statistical techniques that aim to estimate hydrological quantiles at ungauged sites, few approaches were designed to account for the possible nonlinear connections between hydrological variables and catchments characteristics. Recently, a number of nonlinear machine-learning tools have received attention in regional frequency analysis (RFA) applications especially for estimation purposes. In this paper, the aim is to study nonlinearity-related aspects in the RFA of hydrological variables using statistical and machine-learning approaches. To this end, a variety of combinations of linear and nonlinear approaches are considered in the main RFA steps (delineation and estimation). Artificial neural networks (ANNs) and generalized additive models (GAMs) are combined to a nonlinear ANN-based canonical correlation analysis (NLCCA) procedure to ensure an appropriate nonlinear modeling of the complex processes involved. A comparison is carried out between classical linear combinations (CCAs combined with linear regression (LR) model), semilinear combinations (e.g., NLCCA with LR) and fully nonlinear combinations (e.g., NLCCA with GAM). The considered models are applied to three different data sets located in North America. Results indicate that fully nonlinear models (in both RFA steps) are the most appropriate since they provide best performances and a more realistic description of the physical processes involved, even though they are relatively more complex than linear ones. On the other hand, semilinear models which consider nonlinearity either in the delineation or estimation steps showed little improvement over linear models. The linear approaches provided the lowest performances. © 2017. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75770
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, Canada; Institut National de la Recherche Scientifique, Centre Eau Terre et Environnement, Québec, QC, Canada; Institute Centre for Water Advanced Technology and Environmental Research, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates
Recommended Citation:
Ouali D,, Chebana F,, Ouarda T,et al. Fully nonlinear statistical and machine-learning approaches for hydrological frequency estimation at ungauged sites[J]. Journal of Advances in Modeling Earth Systems,2017-01-01,9(2)