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Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt

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  • Mattar, Mohamed A.

Abstract

The Penman-Monteith FAO-56 equation requires the complete climatic records for estimating reference evapotranspiration (ETo). The present study is aimed at developing and evaluating a gene expression programming (GEP) model for estimating mean monthly ETo by using minimal amount of climatic data. The data used in the analysis are collected from 32 weather stations in Egypt through the CLIMWAT database. The results showed that the accuracy of the GEP model significantly improved when either mean relative humidity (RH) or wind speed at 2-m height (u2) was used as additional input variables. The GEP model with the inputs as maximum and minimum air temperature, RH, and u2 showed the lowest root mean square error (0.426 mm d−1 and 0.430 mm d−1) and, the highest coefficient of determination, (0.963 and 0.962) overall index of model performance (0.960 and 0.960), and index of agreement (0.991 and 0.990) for training and testing sets, respectively. Comparing the results of GEP models with other empirical models showed that the GEP technique are more accurate and can be employed successfully in modelling ETo.

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  • Mattar, Mohamed A., 2018. "Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt," Agricultural Water Management, Elsevier, vol. 198(C), pages 28-38.
    • Handle: RePEc:eee:agiwat:v:198:y:2018:i:c:p:28-38
    DOI: 10.1016/j.agwat.2017.12.017
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    1. Xiaoying, Liu & Erda, Lin, 2005. "Performance of the Priestley-Taylor equation in the semiarid climate of North China," Agricultural Water Management, Elsevier, vol. 71(1), pages 1-17, January.
    2. Yassin, Mohamed A. & Alazba, A.A. & Mattar, Mohamed A., 2016. "Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate," Agricultural Water Management, Elsevier, vol. 163(C), pages 110-124.
    3. Landeras, Gorka & Ortiz-Barredo, Amaia & López, Jose Javier, 2008. "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)," Agricultural Water Management, Elsevier, vol. 95(5), pages 553-565, May.
    4. Berti, Antonio & Tardivo, Gianmarco & Chiaudani, Alessandro & Rech, Francesco & Borin, Maurizio, 2014. "Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy," Agricultural Water Management, Elsevier, vol. 140(C), pages 20-25.
    5. Traore, Seydou & Wang, Yu-Min & Kerh, Tienfuan, 2010. "Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone," Agricultural Water Management, Elsevier, vol. 97(5), pages 707-714, May.
    6. Kashyap, P. S. & Panda, R. K., 2001. "Evaluation of evapotranspiration estimation methods and development of crop-coefficients for potato crop in a sub-humid region," Agricultural Water Management, Elsevier, vol. 50(1), pages 9-25, August.
    7. Shiri, Jalal, 2017. "Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran," Agricultural Water Management, Elsevier, vol. 188(C), pages 101-114.
    8. Feng, Yu & Jia, Yue & Cui, Ningbo & Zhao, Lu & Li, Chen & Gong, Daozhi, 2017. "Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China," Agricultural Water Management, Elsevier, vol. 181(C), pages 1-9.
    9. Liu, Xiaoying & Xu, Chunying & Zhong, Xiuli & Li, Yuzhong & Yuan, Xiaohuan & Cao, Jingfeng, 2017. "Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement," Agricultural Water Management, Elsevier, vol. 184(C), pages 145-155.
    10. Martí, Pau & González-Altozano, Pablo & López-Urrea, Ramón & Mancha, Luis A. & Shiri, Jalal, 2015. "Modeling reference evapotranspiration with calculated targets. Assessment and implications," Agricultural Water Management, Elsevier, vol. 149(C), pages 81-90.
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