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A multi-stage methodology for selecting input variables in ANN forecasting of river flows

  • Authors
    Tsekouras G.
    Kousiouris G.
    Panagoulia D.Corresponding
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  • gnest_02067_published.pdf
  • Paper ID
    gnest_02067
  • Paper status
    Published
  • Date paper accepted
  • Date paper online
Abstract

The scientific community has recognized the necessity for more efficiently selected inputs in artificial neural network models (ANNs) in river flows and has worked on this despite some shortcomings. Moreover, there is none or limited inclusion of ANN inputs coupled with atmospheric circulation under various patterns arising from the need of data downscaling for climate change predictions in hydrology domain. This paper presents the results of a novel multi-stage methodology for selecting input variables used in artificial neural network (ANN) models for river flow forecasting. The proposed methodology makes use of data correlations together with a set of crucial statistical indices for optimizing model performance, both in terms of ANN structure (e.g. neurons, momentum rate, learning rate, activation functions, etc), but also in terms of inputs selection. The latter include various previous time steps of daily areal precipitation and temperature data coupled with atmospheric circulation in the form of circulation patterns, observed river flow data and time expressed via functions of sine and cosine. Additionally, the no-linear behavior between river flow and the respective inputs is investigated by the ANN configuration itself and not only by correlation indices (or other equivalent contingency tools). The proposed methodology revealed the river flow of past four days, the precipitation of past three days and the seasonality as robust input variables. However, the temperature of three past days should be considered as an alternative against the seasonality. The produced models forecasting ability was validated by comparing its one-step ahead flow prediction ability to two other approaches (an auto regressive model and a genetic algorithm (GA)-optimized single input ANN).