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Real-Time Flow Forecasting in the Parana River: A Comparison Between Armax and ANN Models

Author(s): Pujol Reig; Lucas; Ortiz; Enrique; Cifres; Enrique; Garc Ia-Bartual; Rafael

Linked Author(s): Jorge Ismael Escobar Ortiz, Rafael Pimentel, Lucas Calvo

Keywords: RMAX; Neural Network; Flow forecast; Real-time

Abstract: Linear and nonlinear approaches are compared herein, in terms of their performance in 5 and 10 days ahead flow forecasts for a given section of the Parana River, located 34 km upstream the city of Santa Fe (Argentina). The models used are an autoregressive moving average with exogenous variable (ARMAX) and a multilayer feedforward artificial neural network (ANN). The variables used as inputs to the models are daily flows at the mentioned section and another cross section located 571 km upstream, together with daily rainfall measured in an intermediate rain gauge station, all during the period 1994-1998. Different configurations of the models have been tested, varying the number of inputs and parameters, but in general the resulting forecast quality with either method is similar and quite satisfactory. Nash Sutcliffe coefficient of efficiency is in all cases over 0. 94 for the 10days ahead forecasts in the validation sample. Among the compared models, best results are obtained with the non linear approach ANN. Some uncertainty considerations are also pointed out, after the analysis of the empirical error distributions under different modelling strategies. It is shown that the apparently centred normal distribution expected for errors really departs significantly from zero-average when different parts of the hydrograph are taken into account.


Year: 2007

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