IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v28y2014i3p657-669.html
   My bibliography  Save this article

Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment

Author

Listed:
  • Ali Rahimikhoob

Abstract

This paper describes a detailed evaluation of the performance and characteristic behaviour of feed-forward artificial neural network (ANN) and M5 model tree for estimating reference evapotranspiration (ET 0 ) at four meteorological sites in an arid climate. The input variables for these models were the maximum and minimum air temperature, air humidity and extraterrestrial radiation. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that the ANN estimated ET 0 better than the M5 model tree but both models performed well for the study area and yielded results close to the FAO56-PM method. Root mean square error and R 2 for the comparison between reference and estimated ET 0 for the tested data using the proposed ANN model are 5.6 % and 0.98, respectively. For the M5 model tree method these values are 8.9 % and 0.98, respectively. The overall results are of significant practical use because the temperature and Humidity-based model can be used when radiation and wind speed data are not available. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Ali Rahimikhoob, 2014. "Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 657-669, February.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:3:p:657-669
    DOI: 10.1007/s11269-013-0506-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-013-0506-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-013-0506-x?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. Seema Chauhan & R. Shrivastava, 2009. "Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 825-837, March.
    2. Utset, Angel & Farre, Imma & Martinez-Cob, Antonio & Cavero, Jose, 2004. "Comparing Penman-Monteith and Priestley-Taylor approaches as reference-evapotranspiration inputs for modeling maize water-use under Mediterranean conditions," Agricultural Water Management, Elsevier, vol. 66(3), pages 205-219, May.
    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. 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.
    2. Rana Muhammad Adnan & Xiaohui Yuan & Ozgur Kisi & Muhammad Adnan & Asif Mehmood, 2018. "Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4469-4486, November.
    3. Dilip Kumar Roy & Tapash Kumar Sarkar & Sujit Kumar Biswas & Bithin Datta, 2023. "Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 193-218, January.
    4. Xiaodong Ren & Zhongyi Qu & Diogo S. Martins & Paula Paredes & Luis S. Pereira, 2016. "Daily Reference Evapotranspiration for Hyper-Arid to Moist Sub-Humid Climates in Inner Mongolia, China: I. Assessing Temperature Methods and Spatial Variability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3769-3791, September.
    5. 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.
    6. Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
    7. Xiaohu Wen & Jianhua Si & Zhibin He & Jun Wu & Hongbo Shao & Haijiao Yu, 2015. "Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3195-3209, July.
    8. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    9. Behrooz Keshtegar & Ozgur Kisi & Hamed Ghohani Arab & Mohammad Zounemat-Kermani, 2018. "Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1101-1116, February.
    10. 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).
    11. Rana Muhammad Adnan & Salim Heddam & Zaher Mundher Yaseen & Shamsuddin Shahid & Ozgur Kisi & Binquan Li, 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches," Sustainability, MDPI, vol. 13(1), pages 1-21, December.
    12. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.
    13. Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.

    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. Liu, Yi & Zeng, Wenzhi & Ao, Chang & Lei, Guoqing & Wu, Jingwei & Huang, Jiesheng & Gaiser, Thomas & Srivastava, Amit Kumar, 2022. "Optimization of winter irrigation management for salinized farmland using a coupled model of soil water flow and crop growth," Agricultural Water Management, Elsevier, vol. 270(C).
    2. Safar Marofi & Hossein Tabari & Hamid Abyaneh, 2011. "Predicting Spatial Distribution of Snow Water Equivalent Using Multivariate Non-linear Regression and Computational Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1417-1435, March.
    3. Ozgur Kisi & Levent Latifoğlu & Fatma Latifoğlu, 2014. "Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4045-4057, September.
    4. Gong, Xuewen & Qiu, Rangjian & Ge, Jiankun & Bo, Guokui & Ping, Yinglu & Xin, Qingsong & Wang, Shunsheng, 2021. "Evapotranspiration partitioning of greenhouse grown tomato using a modified Priestley–Taylor model," Agricultural Water Management, Elsevier, vol. 247(C).
    5. Hlavinka, Petr & Trnka, Miroslav & Balek, Jan & Semerádová, Daniela & Hayes, Michael & Svoboda, Mark & Eitzinger, Josef & Mozný, Martin & Fischer, Milan & Hunt, Eric & Zalud, Zdenek, 2011. "Development and evaluation of the SoilClim model for water balance and soil climate estimates," Agricultural Water Management, Elsevier, vol. 98(8), pages 1249-1261, May.
    6. Ali Rahimikhoob & Mahmood Behbahani & Javad Fakheri, 2012. "An Evaluation of Four Reference Evapotranspiration Models in a Subtropical Climate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2867-2881, August.
    7. Li, Xianyue & Yang, Peiling & Ren, Shumei & Li, Yunkai & Liu, Honglu & Du, Jun & Li, Pingfeng & Wang, Caiyuan & Ren, Liang, 2010. "Modeling cherry orchard evapotranspiration based on an improved dual-source model," Agricultural Water Management, Elsevier, vol. 98(1), pages 12-18, December.
    8. Sungwon Kim & Jalal Shiri & Ozgur Kisi, 2012. "Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3231-3249, September.
    9. Qiu, Rangjian & Liu, Chunwei & Cui, Ningbo & Wu, Youjie & Wang, Zhenchang & Li, Gen, 2019. "Evapotranspiration estimation using a modified Priestley-Taylor model in a rice-wheat rotation system," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    10. Ali Rahimikhoob & Maryam Asadi & Mahmood Mashal, 2013. "A Comparison Between Conventional and M5 Model Tree Methods for Converting Pan Evaporation to Reference Evapotranspiration for Semi-Arid Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4815-4826, November.
    11. Wenjuan Liu & Yang Hong & Sadiq Khan & Mingbin Huang & Trevor Grout & Pradeep Adhikari, 2011. "Evaluation of Global Daily Reference ET Using Oklahoma’s Environmental Monitoring Network—MESONET," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1601-1613, April.
    12. Matin Ahooghalandari & Mehdi Khiadani & Mina Esmi Jahromi, 2016. "Developing Equations for Estimating Reference Evapotranspiration in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3815-3828, September.
    13. Mohamed K. Abdel-Fattah & Sameh Kotb Abd-Elmabod & Zhenhua Zhang & Abdel-Rhman M. A. Merwad, 2023. "Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions," Sustainability, MDPI, vol. 15(21), pages 1-15, October.
    14. Vishwakarma, Dinesh Kumar & Pandey, Kusum & Kaur, Arshdeep & Kushwaha, N.L. & Kumar, Rohitashw & Ali, Rawshan & Elbeltagi, Ahmed & Kuriqi, Alban, 2022. "Methods to estimate evapotranspiration in humid and subtropical climate conditions," Agricultural Water Management, Elsevier, vol. 261(C).
    15. Alexandris, S. & Kerkides, P. & Liakatas, A., 2006. "Daily reference evapotranspiration estimates by the "Copais" approach," Agricultural Water Management, Elsevier, vol. 82(3), pages 371-386, April.
    16. 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.
    17. Utset, Angel & Martinez-Cob, Antonio & Farre, Imma & Cavero, Jose, 2006. "Simulating the effects of extreme dry and wet years on the water use of flooding-irrigated maize in a Mediterranean landplane," Agricultural Water Management, Elsevier, vol. 85(1-2), pages 77-84, September.
    18. Dokoohaki, Hamze & Gheysari, Mahdi & Mousavi, Sayed-Farhad & Zand-Parsa, Shahrokh & Miguez, Fernando E. & Archontoulis, Sotirios V. & Hoogenboom, Gerrit, 2016. "Coupling and testing a new soil water module in DSSAT CERES-Maize model for maize production under semi-arid condition," Agricultural Water Management, Elsevier, vol. 163(C), pages 90-99.
    19. Süleyman Özhan & Ferhat Gökbulak & Yusuf Serengil & Mehmet Özcan, 2010. "Evapotranspiration from a Mixed Deciduous Forest Ecosystem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2353-2363, August.
    20. Ding, Risheng & Kang, Shaozhong & Zhang, Yanqun & Hao, Xinmei & Tong, Ling & Du, Taisheng, 2013. "Partitioning evapotranspiration into soil evaporation and transpiration using a modified dual crop coefficient model in irrigated maize field with ground-mulching," Agricultural Water Management, Elsevier, vol. 127(C), pages 85-96.

    More about this item

    Keywords

    M5 model tree; Neural networks; Reference evapotranspiration; Arid environment;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

    Statistics

    Access and download statistics

    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:spr:waterr:v:28:y:2014:i:3:p:657-669. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.