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Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)

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Cited by:

  1. Hossein Tabari, 2010. "Evaluation of Reference Crop Evapotranspiration Equations in Various Climates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2311-2337, August.
  2. 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.
  3. 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.
  4. Shih-Lun Fang & Yi-Shan Lin & Sheng-Chih Chang & Yi-Lung Chang & Bing-Yun Tsai & Bo-Jein Kuo, 2024. "Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables," Agriculture, MDPI, vol. 14(4), pages 1-20, March.
  5. 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.
  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. 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. 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.
  9. Zou, Ping & Yang, Jingsong & Fu, Jianrong & Liu, Guangming & Li, Dongshun, 2010. "Artificial neural network and time series models for predicting soil salt and water content," Agricultural Water Management, Elsevier, vol. 97(12), pages 2009-2019, November.
  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).
  11. 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.
  12. 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).
  13. 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.
  14. Vásquez, Cristina & Célleri, Rolando & Córdova, Mario & Carrillo-Rojas, Galo, 2022. "Improving reference evapotranspiration (ETo) calculation under limited data conditions in the high Tropical Andes," Agricultural Water Management, Elsevier, vol. 262(C).
  15. 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).
  16. Gafurov, Zafar & Eltazarov, S. & Akramov, Bekzod & Yuldashev, Tulkun & Djumaboev, Kakhramon & Anarbekov, Oyture, 2018. "Modifying Hargreaves-Samani equation for estimating reference evapotranspiration in dryland regions of Amudarya River Basin," Papers published in Journals (Open Access), International Water Management Institute, pages 9(10):1354-.
  17. 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).
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. Shih-Lun Fang & Ting-Jung Chang & Yuan-Kai Tu & Han-Wei Chen & Min-Hwi Yao & Bo-Jein Kuo, 2022. "Plant-Response-Based Control Strategy for Irrigation and Environmental Controls for Greenhouse Tomato Seedling Cultivation," Agriculture, MDPI, vol. 12(5), pages 1-17, April.
  23. 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.
  24. 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.
  25. 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.
  26. Yamaç, Sevim Seda & Şeker, Cevdet & Negiş, Hamza, 2020. "Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area," Agricultural Water Management, Elsevier, vol. 234(C).
  27. Tianao Wu & Wei Zhang & Xiyun Jiao & Weihua Guo & Yousef Alhaj Hamoud, 2020. "Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
  28. 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.
  29. 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.
  30. 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.
  31. Martí, Pau & Gasque, María, 2010. "Ancillary data supply strategies for improvement of temperature-based ETo ANN models," Agricultural Water Management, Elsevier, vol. 97(7), pages 939-955, July.
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