Publication Date:
2015-06-04
Description:
We propose a novel technique for improving a long-term multi-step-ahead streamflow forecasts. A model based on wavelet decomposition and a multivariate Bayesian machine learning approach is developed for forecasting the streamflow three, six, nine and twelve 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 to the wavelet and artificial neural networks based model. The robustness of the models is evaluated. This article is protected by copyright. All rights reserved.
Print ISSN:
0885-6087
Electronic ISSN:
1099-1085
Topics:
Architecture, Civil Engineering, Surveying
,
Geography
