Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment

This paper describes a detailed evaluation of the performance and characteristic behaviour of feed-forward artificial neural network (ANN) and M5 model tree for estimating reference evapotranspiration (ET<Subscript>0</Subscript>) at four meteorological sites in an arid climate. The input variables for these models were the maximum and minimum air temperature, air humidity and extraterrestrial radiation. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that the ANN estimated ET<Subscript>0</Subscript> better than the M5 model tree but both models performed well for the study area and yielded results close to the FAO56-PM method. Root mean square error and R<Superscript>2</Superscript> for the comparison between reference and estimated ET<Subscript>0</Subscript> for the tested data using the proposed ANN model are 5.6 % and 0.98, respectively. For the M5 model tree method these values are 8.9 % and 0.98, respectively. The overall results are of significant practical use because the temperature and Humidity-based model can be used when radiation and wind speed data are not available. Copyright Springer Science+Business Media Dordrecht 2014