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|Title:||Implementation of evolutionary computing models for reference evapotranspiration modeling : short review, assessment and possible future research directions||Authors:||Jing, W
State of the art
Evolutionary computing models
Future research directions
|Issue Date:||2019||Publisher:||Taylor & Francis||Source:||Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 811-823 How to cite?||Journal:||Engineering applications of computational fluid mechanics||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.||URI:||http://hdl.handle.net/10397/81366||ISSN:||1994-2060||EISSN:||1997-003X||DOI:||10.1080/19942060.2019.1645045||Rights:||© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Wang Jing, Zaher Mundher Yaseen, Shamsuddin Shahid, Mandeep Kaur Saggi, Hai Tao, Ozgur Kisi, Sinan Q. Salih, Nadhir Al-Ansari & Kwok-Wing Chau (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, 811-823 is available at https://dx.doi.org/10.1080/19942060.2019.1645045
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