IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v163y2016icp110-124.html
   My bibliography  Save this article

Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate

Author

Listed:
  • Yassin, Mohamed A.
  • Alazba, A.A.
  • Mattar, Mohamed A.

Abstract

Artificial neural networks (ANNs) and gene expression programming (GEP) were compared to estimate daily reference evapotranspiration (ETref) under arid conditions. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The ANN and GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman–Monteith (PMG) model was used as a reference target for evapotranspiration values, with hc varies from 5 to 105cm with increment of a centimetre. The developed models were spatially validated using climatic data from 1980 to 2010 taken from another six meteorological stations. The results showed that the eight ETref models developed using the ANN technique were slightly more accurate than those developed using the GEP technique. The ANN models’ determination coefficients (R2) ranged from 67.6% to 99.8% and root mean square error (RMSE) values ranged from 0.20 to 2.95mmd-1. The GEP models’ R2 values ranged from 64.4% to 95.5% and RMSE values ranged from 1.13 to 3.1mmd-1. Although the GEP models performed slightly worse than the ANN models, the GEP models used explicit equations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:163:y:2016:i:c:p:110-124
    DOI: 10.1016/j.agwat.2015.09.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377415301013
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2015.09.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Mattar, M.A. & Alazba, A.A. & Zin El-Abedin, T.K., 2015. "Forecasting furrow irrigation infiltration using artificial neural networks," Agricultural Water Management, Elsevier, vol. 148(C), pages 63-71.
    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. Gavilan, Pedro & Berengena, Joaquin & Allen, Richard G., 2007. "Measuring versus estimating net radiation and soil heat flux: Impact on Penman-Monteith reference ET estimates in semiarid regions," Agricultural Water Management, Elsevier, vol. 89(3), pages 275-286, May.
    5. Hazi Azamathulla & Aminuddin Ghani & Cheng Leow & Chun Chang & Nor Zakaria, 2011. "Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2901-2916, September.
    6. Chiew, F. H. S. & Kamaladasa, N. N. & Malano, H. M. & McMahon, T. A., 1995. "Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia," Agricultural Water Management, Elsevier, vol. 28(1), pages 9-21, August.
    7. 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.
    8. Gavilan, P. & Lorite, I.J. & Tornero, S. & Berengena, J., 2006. "Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment," Agricultural Water Management, Elsevier, vol. 81(3), pages 257-281, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammadi, Babak & Mehdizadeh, Saeid, 2020. "Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 237(C).
    2. Hong, Taehoon & Jeong, Kwangbok & Koo, Choongwan, 2018. "An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms," Applied Energy, Elsevier, vol. 228(C), pages 808-820.
    3. Al-Ghobari, Hussein M. & El-Marazky, Mohamed S. & Dewidar, Ahmed Z. & Mattar, Mohamed A., 2018. "Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques," Agricultural Water Management, Elsevier, vol. 195(C), pages 211-221.
    4. 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.
    5. 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.
    6. Narjes Nabipour & Nader Karballaeezadeh & Adrienn Dineva & Amir Mosavi & Danial Mohammadzadeh S. & Shahaboddin Shamshirband, 2019. "Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement," Mathematics, MDPI, vol. 7(12), pages 1-22, December.
    7. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2021. "Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine," Agricultural Water Management, Elsevier, vol. 243(C).
    8. Imran Ali Lakhiar & Haofang Yan & Chuan Zhang & Guoqing Wang & Bin He & Beibei Hao & Yujing Han & Biyu Wang & Rongxuan Bao & Tabinda Naz Syed & Junaid Nawaz Chauhdary & Md. Rakibuzzaman, 2024. "A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints," Agriculture, MDPI, vol. 14(7), pages 1-40, July.
    9. Wang, Sheng & Lian, Jinjiao & Peng, Yuzhong & Hu, Baoqing & Chen, Hongsong, 2019. "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China," Agricultural Water Management, Elsevier, vol. 221(C), pages 220-230.
    10. Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
    11. Bellido-Jiménez, Juan A. & Estévez, Javier & García-Marín, Amanda P., 2022. "A regional machine learning method to outperform temperature-based reference evapotranspiration estimations in Southern Spain," Agricultural Water Management, Elsevier, vol. 274(C).
    12. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).
    13. Milan Gocić & Mohammad Arab Amiri, 2021. "Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1913-1926, April.
    14. Mattar, Mohamed A. & Roy, Dilip Kumar & Al-Ghobari, Hussein M. & Dewidar, Ahmed Z., 2022. "Machine learning and regression-based techniques for predicting sprinkler irrigation's wind drift and evaporation losses," Agricultural Water Management, Elsevier, vol. 265(C).
    15. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
    16. Phon Sheng Hou & Lokman Mohd Fadzil & Selvakumar Manickam & Mahmood A. Al-Shareeda, 2023. "Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    17. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
    18. Bhowmik, Mrinal & Muthukumar, P. & Anandalakshmi, R., 2019. "Experimental based multilayer perceptron approach for prediction of evacuated solar collector performance in humid subtropical regions," Renewable Energy, Elsevier, vol. 143(C), pages 1566-1580.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Bellido-Jiménez, Juan Antonio & Estévez, Javier & García-Marín, Amanda Penélope, 2021. "New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain," Agricultural Water Management, Elsevier, vol. 245(C).
    3. 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.
    4. M. Majidi & A. Alizadeh & M. Vazifedoust & A. Farid & T. Ahmadi, 2015. "Analysis of the Effect of Missing Weather Data on Estimating Daily Reference Evapotranspiration Under Different Climatic Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2107-2124, May.
    5. O.E. Mohawesh, 2011. "Evaluation of evapotranspiration models for estimating daily reference evapotranspiration in arid and semiarid environments," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 57(4), pages 145-152.
    6. Cruz-Blanco, M. & Lorite, I.J. & Santos, C., 2014. "An innovative remote sensing based reference evapotranspiration method to support irrigation water management under semi-arid conditions," Agricultural Water Management, Elsevier, vol. 131(C), pages 135-145.
    7. 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.
    8. Osama Mohawesh, 2010. "Spatio-temporal Calibration of Blaney–Criddle Equation in Arid and Semiarid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2187-2201, August.
    9. Traore, Seydou & Luo, Yufeng & Fipps, Guy, 2016. "Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages," Agricultural Water Management, Elsevier, vol. 163(C), pages 363-379.
    10. Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
    11. Jabloun, M. & Sahli, A., 2008. "Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data: Application to Tunisia," Agricultural Water Management, Elsevier, vol. 95(6), pages 707-715, June.
    12. Houshang Ghamarnia & Vahid Rezvani & Erfan Khodaei & Hossein Mirzaei, 2012. "Time and Place Calibration of the Hargreaves Equation for Estimating Monthly Reference Evapotranspiration under Different Climatic Conditions," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 4(3), pages 111-111, January.
    13. Yang, Yang & Luo, Yufeng & Wu, Conglin & Zheng, Hezhen & Zhang, Lei & Cui, Yuanlai & Sun, Ningning & Wang, Li, 2019. "Evaluation of six equations for daily reference evapotranspiration estimating using public weather forecast message for different climate regions across China," Agricultural Water Management, Elsevier, vol. 222(C), pages 386-399.
    14. Alberto Mirambell & Clayton Silva & Flavio Barbosa & Celso Ribeiro, 2017. "A Methodological Proposal Based on Artificial Neural Networks for Evapotranspiration Assessment," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 9(5), pages 142-142, April.
    15. Junzeng Xu & Junmei Wang & Qi Wei & Yanhua Wang, 2016. "Symbolic Regression Equations for Calculating Daily Reference Evapotranspiration with the Same Input to Hargreaves-Samani in Arid China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 2055-2073, April.
    16. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    17. Althoff, Daniel & Filgueiras, Roberto & Dias, Santos Henrique Brant & Rodrigues, Lineu Neiva, 2019. "Impact of sum-of-hourly and daily timesteps in the computations of reference evapotranspiration across the Brazilian territory," Agricultural Water Management, Elsevier, vol. 226(C).
    18. Seydou Traore & Yufeng Luo & Guy Fipps, 2017. "Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4891-4908, December.
    19. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
    20. Ignacio Lorite & Margarita García-Vila & María-Ascensión Carmona & Cristina Santos & María-Auxiliadora Soriano, 2012. "Assessment of the Irrigation Advisory Services’ Recommendations and Farmers’ Irrigation Management: A Case Study in Southern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2397-2419, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:163:y:2016:i:c:p:110-124. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.