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Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions

Jing, Wang and Yaseen, Zaher Mundher and Shamsuddin, Shahid and Saggi, Mandeep Kaur and Tao, H. and Kisi, Ozgur and Salih, Sinan Q. and Al-Ansari, Nadhir and Chau, Kwok-Wing (2019) Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions. Engineering Applications of Computational Fluid Mechanics, 13 (1). pp. 811-823. ISSN 1994-2060

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Abstract

Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ETo) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ETo is a major concern in hydrology. ETo can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ETo. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ETo. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ETo. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ETo in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ETo using EC.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Evapotranspiration prediction; State of the art; Evolutionary computing models; Input variability; Future research directions
Subjects: Q Science > QA Mathematics > QA76 Computer software
Q Science > QC Physics
T Technology > TD Environmental technology. Sanitary engineering
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Institute of Postgraduate Studies
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 17 Jan 2020 02:01
Last Modified: 17 Jan 2020 02:01
URI: http://umpir.ump.edu.my/id/eprint/27444
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