Multiple linear regression and artificial neural networks models for generalized reservoir storage-yield-reliability function for reservoir planning

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    Abstract

    Generalized models for predicting the storage-yield-reliability functions of surface water reservoirs are developed using multiple linear regression and multilayer perceptron, artificial neural networks (ANNs). Linear regression was used to model the total capacity using the over-year capacity as one of the inputs. However, the ANNs were used to simultaneously model directly the intrinsically nonlinear over-year and total (i.e., within-year+over-year) capacity-yield-reliability functions. The inputs used for the ANNs were basic runoff and systems variables such as the coefficient of variation of annual and monthly runoff, minimum monthly runoff, the demand ratio, and reliability. The results showed that all the models performed well during development and when tested with independent data sets. Both models offer avenues for predicting reservoir capacity at gauged sites without the expense of time-series based simulation alternatives. © 2009 ASCE.

    Original languageEnglish
    Pages (from-to)731-738
    Number of pages8
    JournalJournal of Hydrologic Engineering
    Volume14
    Issue number7
    DOIs
    Publication statusPublished - 2009

    Keywords

    • Algorithms
    • Neural networks
    • Regression models
    • Reservoirs
    • Water storage

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