Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing

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The improvement of methods for estimating reference evapotranspiration (ET0) requiring few climatic inputs is crucial, due to the partial or total lack of climatic inputs in many situations. The current paper compares the effect of local and external training procedures in neuro-fuzzy and neural network models for estimating ET0 relying on two input combinations considering k-fold testing. Therefore, different data set configurations were defined based on temporal and spatial criteria allowing for a complete and suitable testing scan of the complete data set. The proposed methodology enabled the comparison in each station of models trained with local data series and models trained with the data series from the remaining stations. Results showed that the external training based on a suitable input choice and a representative pattern collection might be a valid alternative to the more common local training.

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