作 者：Fang, WeiHuang, ShengzhiRen, KunHuang, QiangHuang, GuoheCheng, GuanhuiLi, Kailong
作者机构：State Key Laboratory of Eco-hydraulics in Northwest Arid Region of ChinaXi'an University of Technology Xi'an710048 ChinaNorth China University of Water Resources and Electric PowerZhengzhou450045 ChinaInstitute for EnergyEnvironment and Sustainable Communities University of Regina ReginaSaskatchewanS4S 0A2 Canada
出 版 物：《Journal of Hydrology》
年 卷 期：2019年第568卷
页 面：534-550
核心收录：
中图分类：P33[天文学、地球科学-地球物理学]
学科分类：08[工学]0815[工学-水利工程]081501[工学-水文学及水资源]
基 金：This study was joint funded by the National Key Research and Development Program of China (grant number 2017YFC0405900 )National Natural Science Foundation of China (grant number 51709221 )the Planning project of science and technology of water resources of Shaanxi (grant number 2017slkj-19 )China Scholarship Council (grant number 201608610170 )the doctorate innovation funding of Xi'an University of Technology (grant number 310-252071712 ) and the project of School of Water Resources and Hydropower of Xi'an University of Technology (grant number 2016ZZKT-15 ). Appendix A A.1
主 题：Wavelet decompositionCalibrationDiscrete wavelet transformsForecastingSignal reconstructionStream flowVariational techniquesDecomposition strategyDecomposition techniqueEmpirical Mode DecompositionExplanatory variablesMode decompositionNonstationaritiesStreamflow forecastingTwo stage calibrations
摘 要：The applicability of the traditionally used overall decomposition-based (ODB) sampling technique in the development of forecasting models is controversial. This study first conducts a systematic investigation of the performance of models developed using the ODB sampling technique. A stepwise decomposition-based (SDB) sampling technique that is consistent with actual forecasting practice is then proposed. Moreover, a novel calibration algorithm that couples a two-stage calibration strategy with a shuffled complex evolutionary approach is formulated to help maintain the performance of models. The application of models produced using these different sampling techniques to four gauging stations in China and Canada indicates that (1) the ODB sampling technique that employ the discrete wavelet transform (DWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) as series decomposition techniques do not produce convincing forecasting models because additional information on the future streamflow that is to be predicted is introduced into the explanatory variables of the samples; (2) the SDB sampling technique strictly excludes information on future streamflow from the explanatory variables and is thus as an appropriate alternative for developing forecasting models; (3) the DWT and VMD techniques benefit models by enhancing their performance; on the other hand, the EMD is unsuitable for use in forecasting, due to the variable number of subseries that result from the implementation of the stepwise decomposition strategy. Finally, methods that can be used to enhance the performance of decomposition-based models and the prediction accuracy of nonstationary streamflow are suggested. © 2018 Elsevier B.V.
