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DOI | 10.1007/s00382-017-3525-0 |
KNN-based local linear regression for the analysis and simulation of low flow extremes under climatic influence | |
Lee, Taesam1; Ouarda, Taha B. M. J.2,3; Yoon, Sunkwon4 | |
2017-11-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2017 |
卷号 | 49 |
文章类型 | Article |
语种 | 英语 |
国家 | South Korea; U Arab Emirates; Canada |
英文摘要 | Climate change frequently causes highly nonlinear and irregular behaviors in hydroclimatic systems. The stochastic simulation of hydroclimatic variables reproduces such irregular behaviors and is beneficial for assessing their impact on other regimes. The objective of the current study is to propose a novel method, a k-nearest neighbor (KNN) based on the local linear regression method (KLR), to reproduce nonlinear and heteroscedastic relations in hydroclimatic variables. The proposed model was validated with a nonlinear, heteroscedastic, lag-1 time dependent test function. The validation results of the test function show that the key statistics, nonlinear dependence, and heteroscedascity of the test data are reproduced well by the KLR model. In contrast, a traditional resampling technique, KNN resampling (KNNR), shows some biases with respect to key statistics, such as the variance and lag-1 correlation. Furthermore, the proposed KLR model was used to simulate the annual minimum of the consecutive 7-day average daily mean flow (Min7D) of the Romaine River, Quebec. The observed and extended North Atlantic Oscillation (NAO) index is incorporated into the model. The case study results of the observed period illustrate that the KLR model sufficiently reproduced key statistics and the nonlinear heteroscedasticity relation. For the future period, a lower mean is observed, which indicates that drier conditions other than normal might be expected in the next decade in the Romaine River. Overall, it is concluded that the KLR model can be a good alternative for simulating irregular and nonlinear behaviors in hydroclimatic variables. |
英文关键词 | Hydropower k-Nearest neighbor Local linear regression Min7D flow Nonparametric model Stochastic simulation |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000414153800032 |
WOS关键词 | HYDROLOGIC TIME-SERIES ; DAILY RAINFALL ; NONPARAMETRIC APPROACH ; STREAMFLOW SIMULATION ; WEATHER GENERATOR ; CHANGE SCENARIOS ; NEURAL-NETWORKS ; ABRUPT CHANGES ; MODEL ; VARIABILITY |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/35545 |
专题 | 气候变化 |
作者单位 | 1.Gyeongsang Natl Univ, Dept Civil Engn, ERI, 501 Jinju Daero, Jinju 52828, Gyeongsangnam D, South Korea; 2.Masdar Inst Sci & Technol, Inst Ctr Water Adv Technol & Environm Res iWATER, POB 54224, Abu Dhabi, U Arab Emirates; 3.INRS ETE, Natl Inst Sci Res, 490 Couronne, Quebec City, PQ 490, Canada; 4.APEC Climate Ctr, Climate Res Dept, Busan 48058, South Korea |
推荐引用方式 GB/T 7714 | Lee, Taesam,Ouarda, Taha B. M. J.,Yoon, Sunkwon. KNN-based local linear regression for the analysis and simulation of low flow extremes under climatic influence[J]. CLIMATE DYNAMICS,2017,49. |
APA | Lee, Taesam,Ouarda, Taha B. M. J.,&Yoon, Sunkwon.(2017).KNN-based local linear regression for the analysis and simulation of low flow extremes under climatic influence.CLIMATE DYNAMICS,49. |
MLA | Lee, Taesam,et al."KNN-based local linear regression for the analysis and simulation of low flow extremes under climatic influence".CLIMATE DYNAMICS 49(2017). |
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