Machine-Learning Models to Improve Accuracy of Real-Time Reference Evapotranspiration Estimates in an Arid Environment-Reference-Cited by-同舟云学术

Machine-Learning Models to Improve Accuracy of Real-Time Reference Evapotranspiration Estimates in an Arid Environment

Published:2022-11 Issue:11 Volume:148 Page:
ISSN:0733-9437
Container-title:Journal of Irrigation and Drainage Engineering
language:en
Short-container-title:J. Irrig. Drain Eng.

Author:

Pirvali Beiranvand Javad¹,Ghamghami Mahdi²

Affiliation:

1. Assistant Professor, Dept. of Water and Soil Sciences and Engineering, Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute, Karaj 00982, Iran (corresponding author).

2. Postdoctoral Researcher, Dept. of Water and Soil Sciences and Engineering, Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute, Karaj 00982, Iran.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Agricultural and Biological Sciences (miscellaneous),Water Science and Technology,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IR.1943-4774.0001714

Reference72 articles.

1. Extreme Learning Machines: A new approach for prediction of reference evapotranspiration

2. Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran

3. Assessment of eight reference evapotranspiration (ETo) methods considering Köppen climate class in Iran

4. Allen, R. G., L. S. Pereira, D. Raes, and M. Smith. 1998. “Crop evapotranspiration.” In Guide lines for computing crop evapotranspiration. Food and Agriculture Organization (FAO) irrigation and drainage paper no. 56. Rome: FAO.

5. An update for the definition of reference evapotranspiration;Allen R. G.;ICID Bull.,1994

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