Quantification of parametric uncertainty of ANN models with GLUE method for different streamflow dynamics

Hakan Tongal*, Martijn J. Booij

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)

Abstract

This study describes the parametric uncertainty of artificial neural networks (ANNs) by employing the generalized likelihood uncertainty estimation (GLUE) method. The ANNs are used to forecast daily streamflow for three sub-basins of the Rhine Basin (East Alpine, Main, and Mosel) having different hydrological and climatological characteristics. We have obtained prior parameter distributions from 5000 ANNs in the training period to capture the parametric uncertainty and subsequently 125,000 correlated parameter sets were generated. These parameter sets were used to quantify the uncertainty in the forecasted streamflow in the testing period using three uncertainty measures: percentage of coverage, average relative length, and average asymmetry degree. The results indicated that the highest uncertainty was obtained for the Mosel sub-basin and the lowest for the East Alpine sub-basin mainly due to hydro-climatic differences between these basins. The prediction results and uncertainty estimates of the proposed methodology were compared to the direct ensemble and bootstrap methods. The GLUE method successfully captured the observed discharges with the generated prediction intervals, especially the peak flows. It was also illustrated that uncertainty bands are sensitive to the selection of the threshold value for the Nash–Sutcliffe efficiency measure used in the GLUE method by employing the Wilcoxon–Mann–Whitney test.

Original languageEnglish
Pages (from-to)993-1010
Number of pages18
JournalStochastic environmental research and risk assessment
Volume31
Issue number4
DOIs
Publication statusPublished - 1 May 2017

Keywords

  • Artificial neural networks
  • Bootstrap
  • Generalized likelihood uncertainty estimation (GLUE)
  • Parametric uncertainty
  • Rhine Basin
  • Uncertainty measures

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