Table of Contents
Journal of Climatology
Volume 2014 (2014), Article ID 839205, 11 pages
Research Article

Comparative Study of M5 Model Tree and Artificial Neural Network in Estimating Reference Evapotranspiration Using MODIS Products

Department of Irrigation and Drainage Engineering, College of Aburaihan, University of Tehran, P.O. Box 33955-159, Pakdasht, Tehran, Iran

Received 29 August 2014; Accepted 18 November 2014; Published 17 December 2014

Academic Editor: Ines Alvarez

Copyright © 2014 Armin Alipour et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Reference evapotranspiration () is one of the major parameters affecting hydrological cycle. Use of satellite images can be very helpful to compensate for lack of reliable weather data. This study aimed to determine using land surface temperature (LST) data acquired from MODIS sensor. LST data were considered as inputs of two data-driven models including artificial neural network (ANN) and M5 model tree to estimate values and their results were compared with calculated by FAO-Penman-Monteith (FAO-PM) equation. Climatic data of five weather stations in Khuzestan province, which is located in the southeastern Iran, were employed in order to calculate . LST data extracted from corresponding points of MODIS images were used in training of ANN and M5 model tree. Among study stations, three stations (Amirkabir, Farabi, and Gazali) were selected for creating the models and two stations (Khazaei and Shoeybie) for testing. In Khazaei station, the coefficient of determination () values for comparison between calculated by FAO-PM and estimated by ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner, values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimate by means of LST data derived from MODIS sensor.