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Adjusting wavelet‐based multiresolution analysis boundary conditions for long‐term streamflow forecasting
Author(s) -
Maslova I.,
Ticlavilca A. M.,
McKee M.
Publication year - 2015
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.10564
Subject(s) - streamflow , wavelet , computer science , robustness (evolution) , artificial intelligence , bayesian probability , bayesian inference , multivariate statistics , term (time) , data mining , machine learning , drainage basin , geography , cartography , physics , quantum mechanics , biochemistry , chemistry , gene
Abstract We propose a novel technique for improving a long‐term multi‐step‐ahead streamflow forecast. A model based on wavelet decomposition and a multivariate Bayesian machine learning approach is developed for forecasting the streamflow 3, 6, 9, and 12 months ahead simultaneously. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model accuracy can be increased by using the wavelet boundary rule introduced in this study. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data from the Yellowstone River in the Uinta Basin in Utah. The model based on the combination of wavelet and Bayesian machine learning regression techniques is compared with that of the wavelet and artificial neural networks‐based model. The robustness of the models is evaluated. Copyright © 2015 John Wiley & Sons, Ltd.