Applicability Of A 4 Inputs ANN Model For
ETo Prediction In Coastal And Inland Locations
Author(s)
P. Martí, A. Royuela, J. Manzano & G. Palau
Abstract
Artificial Neural Networks (ANNs) are simplified models of the central nervous
system that can be used as effective tools to model nonlinear problems. This paper
reports the study of the applicability of an ANN-based ETo predicting-model in
different geographical contexts of the Valencia region. The proposed model only
demands the measurement of the maximum and minimum daily air temperatures
and the calculation of extraterrestrial radiation and daylight hours. The model
provides acceptable approximations of Penman-Monteith ETo values, better than
already existing ETo predicting tools (Hargreaves), for coastal locations, where the
sea contributes to hinder drastic climatological fluctuations. Nevertheless, the
mapping capability of this model in other places with higher indexes of
continentality is to be questioned. Furthermore, the possibility of achieving good
ETo predictions in different inland locations to those used to train the network
might be looked with uncertainty, because of the local uniqueness of the complex
relationships between temperature and ETo. On the other hand, models trained in
coastal locations might be preferable to carry out predictions in inland locations.
The proposed 4-inputs ANN can be useful and preferable to other methods when
ETo models which demand a high number of variables cannot be used.
Keywords: ETo prediction, artificial neural networks, maximum and minimum
temperatures, index of continentality.
1 Introduction
The precise quantification of cropping evapotranspiration has become a very
important task, due to the current water shortage and the subsequent rise of water
price. On the other hand, the already existing models that provide precise enough
Keywords
ETo prediction, artificial neural networks, maximum and minimum
temperatures, index of continentality.
Related Book
Other papers in this volume
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