Álvaro Royuela

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

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.

Generalization of ETo ANN Models through Data Supplanting

This paper describes the application of artificial neural networks (ANNs) for estimating reference evapotranspiration ( ETo ) as a function of local maximum and minimum air temperatures as well as exogenous relative humidity and reference evapotranspiration in different continental contexts of the autonomous Valencia region, on the Spanish Mediterranean coast. The development of new and more precise models for ETo prediction from minimum climatic data is required, since the application of existing methods that provide acceptable results is limited to those places where large amounts of reliable climatic data are available. The Penman-Monteith model for ETo prediction, proposed by the FAO as the sole standard method for ETo estimation, was used to provide the ANN targets for the training and testing processes. Concerning models which demand scant climatic inputs, the proposed model provides performances with lower associated errors than the currently existing temperature-based models, which only consider l...

Integrated Emitter Local Loss Prediction Using Artificial Neural Networks

This paper describes an application of artificial neural networks (ANNs) to the prediction of local losses from integrated emitters. First, the optimum input-output combination was determined. Then, the mapping capability of ANNs and regression models was compared. Afterwards, a five-input ANN model, which considers pipe and emitter internal diameter, emitter length, emitter spacing, and pipe discharge, was used to develop a local losses predicting tool which was obtained from different training strategies while taking into account a completely independent test set. Finally, a performance index was evaluated for the test emitter models studied. Emitter data with low reliability were removed from the process. Performance indexes over 80% were obtained for the remaining test emitters.