Modeling daily evapotranspiration in hyper-arid environment using gene expression programming

Accurate estimations of reference evapotranspiration (ETref) are extremely important for maximizing the beneficial use of water and hydrologic applications, particularly in arid and semiarid regions where water sources are so limited. The aim of this study is to develop mathematical models to calculate the daily ETref using a gene expression programming (GEP) technique. Eight GEP models (GEP-MOD1–8) were developed from combinations of climatic variables. The Penman-Monteith equation was considered the reference method, with the reference plant height varying from 5 to 105 cm in 5-cm increments. Daily climatic variables collected from 13 meteorological stations, one station from every region within the Kingdom of Saudi Arabia, covered the 1980 to 2010 period. Of the available climatic data, 65 % was used in the training process for the eight developed GEP models, and 35 % was used in the testing process. The accuracy of the eight developed GEP models to estimate ETref varied in significance depending on the climatic variables that were included. As more climatic parameters were input, the accuracy of the GEP model increased. For the testing process, the coefficient of determination (R2) ranged from a low of 63.4 % for GEP-MOD1 to a high of 95.4 % for GEP-MOD8, and the root mean square error (RMSE) values ranged from 3.19 mm day−1 for GEP-MOD1 to 1.14 mm day−1 for GEP-MOD8. From the spatial evaluation, the values of RMSE ranged from 3.27 mm day−1 for GEP-MOD1 to 1.21 mm day−1 for GEP-MOD8. In addition, the respective RMSE values resulting from GEP-MOD8 for plant heights of 50 and 12 cm were 0.75 and 0.96 cm. This implies that the developed GEP-MOD8 can be used for any value of the reference plant height ranging from 5 to 105 cm with insignificant errors. Interestingly, solar radiation had an almost insignificant effect on ETref in the hyper-arid conditions. In contrast, wind speed and plant height had a large positive effect on increasing the accuracy of calculating ETref.

[1]  Marnik Vanclooster,et al.  Effect of the sampling frequency of meteorological variables on the estimation of the reference evapotranspiration , 2001 .

[2]  Cândida Ferreira Gene Expression Programming in Problem Solving , 2002 .

[3]  Richard G. Allen,et al.  Measuring versus estimating net radiation and soil heat flux: Impact on Penman-Monteith reference ET estimates in semiarid regions , 2007 .

[4]  Hazi Mohammad Azamathulla,et al.  Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River , 2011 .

[5]  I. A. Walter,et al.  The ASCE standardized reference evapotranspiration equation , 2005 .

[6]  Wang Yumin,et al.  Seasonal temperature-based models for reference evapotranspiration estimation under semi-arid condition of Malawi , 2009 .

[7]  Hossein Tabari,et al.  Regional Estimation of Reference Evapotranspiration in Arid and Semiarid Regions , 2010 .

[8]  G. Hoogenboom,et al.  A comparison of ASCE and FAO-56 reference evapotranspiration for a 15-min time step in humid climate conditions , 2009 .

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

[10]  J. Monteith,et al.  Principles of Environmental Physics , 2014 .

[11]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[12]  Surajit Chattopadhyay,et al.  Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach , 2009 .

[13]  Francis H. S. Chiew,et al.  PENMAN-MONTEITH, FAO-24 REFERENCE CROP EVAPOTRANSPIRATION AND CLASS-A PAN DATA IN AUSTRALIA , 1995 .

[14]  Slavisa Trajkovic,et al.  Comparison of simplified pan-based equations for estimating reference evapotranspiration. , 2010 .

[15]  N. R. Sakthivel,et al.  Soft computing approach to fault diagnosis of centrifugal pump , 2012, Appl. Soft Comput..

[16]  N. Raghuwanshi,et al.  Decision Support System for Estimating Reference Evapotranspiration , 2002 .

[17]  R. Abrahart,et al.  Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models , 2012 .

[18]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[19]  Hamid Reza Fooladmand,et al.  Comparison of different types of Hargreaves equation for estimating monthly evapotranspiration in the south of Iran , 2008 .

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

[21]  Saeed Samadianfard,et al.  Gene expression programming analysis of implicit Colebrook–White equation in turbulent flow friction factor calculation , 2012 .

[22]  Terry J. Gillespie,et al.  Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada , 2010 .

[23]  I. Lorite,et al.  Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment , 2006 .

[24]  Aytac Guven,et al.  Regional-Specific Numerical Models of Evapotranspiration Using Gene-Expression Programming Interface in Sahel , 2012, Water Resources Management.

[25]  Ali Aytek,et al.  New Approach for Stage–Discharge Relationship: Gene-Expression Programming , 2009 .

[26]  Vijay P. Singh,et al.  Cross Comparison of Empirical Equations for Calculating Potential Evapotranspiration with Data from Switzerland , 2002 .

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

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

[29]  Du Zheng,et al.  Radiation calibration of FAO56 Penman-Monteith model to estimate reference crop evapotranspiration in China , 2008 .

[30]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[31]  Marvin E. Jensen,et al.  ASCE's standardized reference evapotranspiration equation. , 2001 .

[32]  Hafzullah Aksoy,et al.  Genetic Programming‐Based Empirical Model for Daily Reference Evapotranspiration Estimation , 2008 .

[33]  G. Fogg The state and movement of water in living organisms. , 1966, Journal of the Marine Biological Association of the United Kingdom.

[34]  O. Kisi,et al.  Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain) , 2012 .

[35]  Hossein Tabari,et al.  Local Calibration of the Hargreaves and Priestley-Taylor Equations for Estimating Reference Evapotranspiration in Arid and Cold Climates of Iran Based on the Penman-Monteith Model , 2011 .

[36]  Ioannis K. Tsanis,et al.  Hydroinformatics in evapotranspiration estimation , 2003, Environ. Model. Softw..

[37]  H. Md. Azamathulla,et al.  Gene-expression programming for transverse mixing coefficient , 2012 .

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

[39]  H. Md. Azamathulla Gene-expression programming to predict friction factor for Southern Italian rivers , 2012, Neural Computing and Applications.

[40]  Ayse Irmak,et al.  Solar and Net Radiation-Based Equations to Estimate Reference Evapotranspiration in Humid Climates , 2003 .

[41]  N. S. Raghuwanshi,et al.  Artificial neural networks approach in evapotranspiration modeling: a review , 2010, Irrigation Science.

[42]  Amin Elshorbagy,et al.  Modelling the dynamics of the evapotranspiration process using genetic programming , 2007 .

[43]  Ozgur Kisi,et al.  Evapotranspiration Modeling Using Linear Genetic Programming Technique , 2010 .

[44]  H. L. Penman Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

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

[46]  Hossein Tabari,et al.  Evaluation of Reference Crop Evapotranspiration Equations in Various Climates , 2010 .

[47]  S. S. Zanetti,et al.  Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data , 2007 .

[48]  Slavisa Trajkovic,et al.  Testing hourly reference evapotranspiration approaches using lysimeter measurements in a semiarid climate. , 2010 .