Napolitano, G.

See, L.

Serinaldi, F.

International Institute for Applied Systems Analysis (IIASA)

Geophysical Research AbstractsVol. 14, EGU2012-11945, 2012EGU General Assembly 2012© Author(s) 2012Examining the impact of Empirical Mode Decomposition in ANNhindcasting of daily stream flow seriesF. Serinaldi (1,2), G. Napolitano (3), L. See (3,4)(1) School of Civil Engineering and Geosciences, Newcastle University, Newcastle Upon Tyne, UK(francesco.serinaldi@ncl.ac.uk), (2) Willis Research Network, London, UK, (3) School of Geography, University of Leeds,Leeds, UK, (4) International Institute of Applied Systems Analysis, Laxenburg, AustriaIn this study, we explore the effects of preprocessing the data through a fully data-driven signal decompositiontechnique, called Empirical Mode Decomposition (EMD), to perform ensemble modeling of daily stream flowseries via artificial neural networks (ANNs). The EMD is used to decompose the original signal into a numberof intrinsic components, which are then trained individually by ANNs and combined to rebuild the dischargeseries. The ANN training is carried out with different sets of initial random weights that are drawn from auniform distribution to assess the effect of the parameter uncertainty on the modeling results. A large numberof performance indices and two formal tests are applied to the model predictions in order to provide a compre-hensive assessment of the model performance. The results show that, in a typical ANN modeling procedure, theadvantages of the signal preprocessing depend on the characteristics of the signal intrinsic modes highlightedby EMD analysis. When the signal is characterized by highly energetic high frequency components, the errorpropagation influences the results so that the ensemble algorithm introduces an improvement only in terms ofsome performance measures, and the uncertainty related to the random generation of the ANN initial weights maybe significant. Therefore, the nature of the signal, the effect of the random initialisation of the weights on ANNmodel performance and the measures which are used for model assessment (and which extend beyond ANNs toother model types) need to be more carefully taken into account in the modeling procedures.ReferencesNapolitano G, Serinaldi F, See L. (2011) Impact of EMD decomposition and random initialisation of weights inANN hindcasting of daily stream flow series: an empirical examination. Journal of Hydrology, 406(3-4), 199-214.

Examining the impact of Empirical Mode Decomposition in ANN hindcasting of daily stream flow series

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