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

Ali Rahimikhoob ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2014, vol. 28, issue 3, 657-669

Abstract: 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 0 ) 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 0 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 2 for the comparison between reference and estimated ET 0 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

Keywords: M5 model tree; Neural networks; Reference evapotranspiration; Arid environment (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/s11269-013-0506-x

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