M5 model tree based modelling of reference evapotranspiration

This paper investigates the potential of M5 model tree based regression approach to model daily reference evapotranspiration using climatic data of Davis station maintained by California irrigation Management Information System (CIMIS). Four inputs including solar radiation, average air temperature, average relative humidity, and average wind speed whereas reference evapotranspiration calculated using a relation provided by the CIMIS was used as output. To compare the performance of M5 model tree in predicting the reference evapotranspiration, FAO–56 Penman–Monteith equation and calibrated Hargreaves–Samani relation was used. A comparison of results suggests that M5 model tree approach works well in comparison to both FAO–56 and calibrated Hargreaves–Samani relations. To judge the generalization capability of M5 model tree approach, model created by using the Davis data set was tested with the datasets of four different sites. Results from this part of the study suggest that M5 model tree could successfully be employed in modeling the reference evapotranspiration. Further, sensitivity analysis with M5 model tree approach suggests the suitability of solar radiation, average air temperature, average relative humidity, and average wind speed as input parameters to model the reference evapotranspiration Copyright © 2009 John Wiley & Sons, Ltd.

[1]  Dimitri P. Solomatine,et al.  M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China , 2004 .

[2]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[3]  Dimitri P. Solomatine,et al.  FOR AN , 2022 .

[4]  Narendra Singh Raghuwanshi,et al.  Estimating Evapotranspiration using Artificial Neural Network , 2002 .

[5]  Dimitri P. Solomatine,et al.  FLEXIBLE AND OPTIMAL M5 MODEL TREES WITH APPLICATIONS TO FLOW PREDICTIONS , 2004 .

[6]  O. Kisi The potential of different ANN techniques in evapotranspiration modelling , 2008 .

[7]  N. Null Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[8]  Ozgur Kisi,et al.  Evapotranspiration estimation using feed-forward neural networks , 2006 .

[9]  Mahesh Pal,et al.  M5 model tree for land cover classification , 2006 .

[10]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[11]  Lakshman Nandagiri,et al.  Performance Evaluation of Reference Evapotranspiration Equations across a Range of Indian Climates , 2006 .

[12]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[13]  Baryohay Davidoff,et al.  Comparison of Some Reference Evapotranspiration Equations for California , 2005 .

[14]  R. Allen,et al.  Evapotranspiration and Irrigation Water Requirements , 1990 .

[15]  D. Solomatine,et al.  Model trees as an alternative to neural networks in rainfall—runoff modelling , 2003 .

[16]  Ozgur Kisi,et al.  Evapotranspiration modelling from climatic data using a neural computing technique , 2007 .

[17]  K. P. Sudheer,et al.  Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique , 2003 .

[18]  Dimitri P. Solomatine,et al.  Mixtures of Simple Models vs ANNs in Hydrological Modeling , 2003, HIS.

[19]  Slavisa Trajkovic,et al.  Temperature-based approaches for estimating reference evapotranspiration , 2005 .

[20]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[21]  Branimir Todorovic,et al.  Forecasting of Reference Evapotranspiration by Artificial Neural Networks , 2003 .

[22]  Ozgur Kisi,et al.  Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation , 2007 .

[23]  Richard G. Allen,et al.  Report. Expert Consultation on Revision of FAO Methodologies for Crop Water Requirements. , 1992 .