Artificial neural network based generalized storage-yield-reliability models using the Levenberg-Marquardt algorithm

A. J. Adeloye, A. D. Munari

    Research output: Contribution to journalArticle

    60 Citations (Scopus)

    Abstract

    Generalised storage-yield-reliability models are developed using multi-layer perceptrons artificial neural networks (ANNs), trained using the Levenberg-Marquardt algorithm. These ANNs provide, for the first time, generalised models for simultaneously predicting within-year and over-year storage capacities, given the yield, reliability and readily obtainable streamflow statistics. The training, validation and testing of the models used time series data from 18 streams located in different parts of the world, which were carefully selected so that they nearly cover the range of flow variability observed in world streams. The performance of the models was very good. Further comparison of the ANN models with existing regression models revealed that the latter are marginally better; however, given that the regression models require the over-year capacity to be known a priori, the ANN models are more generic and should be preferred. © 2006 Elsevier B.V. All rights reserved.

    Original languageEnglish
    Pages (from-to)215-230
    Number of pages16
    JournalJournal of Hydrology
    Volume326
    Issue number1-4
    DOIs
    Publication statusPublished - 15 Jul 2006

    Keywords

    • Artificial neural networks
    • Levenberg-Marquardt
    • Over-year capacity
    • Sequent peak algorithm
    • Storage-yield-reliability
    • Within-year capacity

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