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Assessment of a 4-input artificial neural network for ETo estimation through data set scanning procedures

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Abstract

Evapotranspiration is a complex and non-linear phenomenon that depends on the interaction of several climatic parameters. As an alternative to traditional techniques, artificial neural networks (ANNs) are highly appropriate for the modeling of non-linear processes. In general, in the most common ANN applications, the available climatic series are usually split up into 3 data sets: one for training, one for cross-validating, and one for testing. Up to now, the studies regarding ANN-models for reference evapotranspiration estimation and forecasting consider usually only a single chronological assignment of data for the definition of these 3 data sets. In these cases, the ANN performance can only be referred to this specific data set assignment. This paper analyzes the performance of a simple ANN model, a temperature-based 4-input ANN, taking into consideration a complete scan of the possible training, cross-validation, and test set configurations using ‘leave one out’ procedures. The results of a comparative analysis between both methodologies show that the performance results achieved with the traditional methodology can be misleading when evaluating the real ability of a model, as they are referred to the single specific data set assignment assumed.

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Authors and Affiliations

  1. Centro Valenciano de Estudios sobre el Riego (CVER), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    Pau Martí, Juan Manzano & Álvaro Royuela

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  1. Pau Martí
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  2. Juan Manzano
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  3. Álvaro Royuela
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Correspondence to Pau Martí.

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Communicated by S. Azam-Ali.

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Martí, P., Manzano, J. & Royuela, Á. Assessment of a 4-input artificial neural network for ETo estimation through data set scanning procedures. Irrig Sci 29, 181–195 (2011). https://doi.org/10.1007/s00271-010-0224-6

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  • DOI: https://doi.org/10.1007/s00271-010-0224-6

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