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Wavelet-Based Rainfall–Stream Flow Models for the Southeast Murray Darling Basin
This study compares time series models for stream flow that use lagged rainfall as an exogenous variable with models that use a small subset of discrete wavelet coefficients of lagged rainfall with cross product and quadratic terms of the wavelet coefficients. The models require the calculation of a moving discrete wavelet transform (MDWT) that is implemented as a multiscale transform. A comparison is made using data from three catchments in the Murray Darling Basin in Australia. For finite impulse response models, the adjusted coefficient of determination, (), increased with the MDWT from 0.36 to 0.46 for the Tooma River Basin (TRB), from 0.43 to 0.52 for the Jingellic Catchment (JC), and from 0.56 to 0.59 for the Ovens Catchment (OC). The autoregressive models with exogenous input (ARX) with the wavelet transform further improved these results. The MDWT is based on a Haar wavelet so the wavelet coefficients have a physical interpretation. Predictions are further improved by a two-stage prediction procedure, in which an improved prediction is found as a quadratic function of the original prediction.
Wavelet-Based Rainfall–Stream Flow Models for the Southeast Murray Darling Basin
This study compares time series models for stream flow that use lagged rainfall as an exogenous variable with models that use a small subset of discrete wavelet coefficients of lagged rainfall with cross product and quadratic terms of the wavelet coefficients. The models require the calculation of a moving discrete wavelet transform (MDWT) that is implemented as a multiscale transform. A comparison is made using data from three catchments in the Murray Darling Basin in Australia. For finite impulse response models, the adjusted coefficient of determination, (), increased with the MDWT from 0.36 to 0.46 for the Tooma River Basin (TRB), from 0.43 to 0.52 for the Jingellic Catchment (JC), and from 0.56 to 0.59 for the Ovens Catchment (OC). The autoregressive models with exogenous input (ARX) with the wavelet transform further improved these results. The MDWT is based on a Haar wavelet so the wavelet coefficients have a physical interpretation. Predictions are further improved by a two-stage prediction procedure, in which an improved prediction is found as a quadratic function of the original prediction.
Wavelet-Based Rainfall–Stream Flow Models for the Southeast Murray Darling Basin
Kamruzzaman, Mohammad (Autor:in) / Metcalfe, Andrew V. (Autor:in) / Beecham, Simon (Autor:in)
Journal of Hydrologic Engineering ; 19 ; 1283-1293
10.08.2013
112013-01-01 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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