The relative utility of regression and artificial neural networks models for rapidly predicting the capacity of water supply reservoirs

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    Abstract

    Rapid prediction tools for reservoir over-year and within-year capacities that dispense with the sequential analysis of time-series runoff data are developed using multiple linear regression and multi-layer perceptron, artificial neural networks (MLP-ANNs). Linear regression was used to model the total (i.e. within-year + over-year) capacity using the over-year capacity as one of the inputs, while the ANNs were used to simultaneously model directly the over-year and total capacities. The inputs used for the ANNs were basic runoff and systems variables such as the coefficient of variation (Cv) of annual and monthly runoff, minimum monthly runoff, the demand ratio and reservoir reliability. The results showed that all the models performed well during their development and when they were tested with independent data sets. Both models offer faster prediction tools for reservoir capacity at gauged sites when compared with behaviour simulation. Additionally, when the predictor variables can be evaluated at un-gauged sites using e.g. catchment characteristics, they make capacity estimation at such un-gauged sites a feasible proposition. © 2009 Elsevier Ltd. All rights reserved.

    Original languageEnglish
    Pages (from-to)1233-1240
    Number of pages8
    JournalEnvironmental Modelling and Software
    Volume24
    Issue number10
    DOIs
    Publication statusPublished - Oct 2009

    Keywords

    • Artificial neural networks
    • Multiple regression
    • Over-year capacity
    • Sequent-peak algorithm
    • Storage-yield-reliability
    • Un-gauged sites
    • Within-year capacity

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