Annual streamflow modelling with asymmetric distribution function |
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Authors: | Nermin Sarlak |
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Affiliation: | Department of Civil Engineering, Faculty of Engineering, University of Gaziantep, 27310, Gaziantep, Turkey |
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Abstract: | Classical autoregressive models (AR) have been used for forecasting streamflow data in spite of restrictive assumptions, such as the normality assumption for innovations. The main reason for making this assumption is the difficulties faced in finding model parameters for non‐normal distribution functions. However, the modified maximum likelihood (MML) procedure used for estimating autoregressive model parameters assumes a non‐normally distributed residual series. The aim in this study is to compare the performance of the AR(1) model with asymmetric innovations with that of the classical autoregressive model for hydrological annual data. The models considered are applied to annual streamflow data obtained from two streamflow gauging stations in K?z?l?rmak Basin, Turkey. Copyright © 2008 John Wiley & Sons, Ltd. |
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Keywords: | autoregressive model modified maximum likelihood asymmetric innovation Weibull distribution |
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