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Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Author

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  • Hatice Citakoglu
  • Murat Cobaner
  • Tefaruk Haktanir
  • Ozgur Kisi

Abstract

Monthly mean reference evapotranspiration (ET 0 ) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET 0 given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET 0 values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET 0 , because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Hatice Citakoglu & Murat Cobaner & Tefaruk Haktanir & Ozgur Kisi, 2014. "Estimation of Monthly Mean Reference Evapotranspiration in Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 99-113, January.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:1:p:99-113
    DOI: 10.1007/s11269-013-0474-1
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    References listed on IDEAS

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    1. Seydou Traore & Aytac Guven, 2012. "Regional-Specific Numerical Models of Evapotranspiration Using Gene-Expression Programming Interface in Sahel," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4367-4380, December.
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    1. 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.
    2. Tao, Hai & Diop, Lamine & Bodian, Ansoumana & Djaman, Koffi & Ndiaye, Papa Malick & Yaseen, Zaher Mundher, 2018. "Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso," Agricultural Water Management, Elsevier, vol. 208(C), pages 140-151.
    3. J Sreekanth & Bithin Datta, 2014. "Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2005-2019, May.
    4. 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).
    5. Xinxin He & Jungang Luo & Ganggang Zuo & Jiancang Xie, 2019. "Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1571-1590, March.
    6. 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.
    7. Jusmy D. Putuhena & Asep Sapei, 2015. "Institutional System of Watershed Management in Leitimor Peninsula, Ambon Island (Watershed Management Institution in Ambon Island Peninsula Leitimor)," Modern Applied Science, Canadian Center of Science and Education, vol. 10(1), pages 161-161, January.
    8. K. Durga Rao & Vala Rao & Vinay Dadhwal, 2014. "Improvement to the Thornthwaite Method to Study the Runoff at a Basin Scale Using Temporal Remote Sensing Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(6), pages 1567-1578, April.
    9. Ahmadi, Farshad & Mehdizadeh, Saeid & Mohammadi, Babak & Pham, Quoc Bao & DOAN, Thi Ngoc Canh & Vo, Ngoc Duong, 2021. "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation," Agricultural Water Management, Elsevier, vol. 244(C).
    10. Roy, Dilip Kumar & Lal, Alvin & Sarker, Khokan Kumer & Saha, Kowshik Kumar & Datta, Bithin, 2021. "Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system," Agricultural Water Management, Elsevier, vol. 255(C).

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