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

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 agricu...

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Main Authors:	Jing, Wang, Yaseen, Zaher Mundher, Shamsuddin, Shahid, Saggi, Mandeep Kaur, Tao, H., Kisi, Ozgur, Salih, Sinan Q., Al-Ansari, Nadhir, Chau, Kwok-Wing
Format:	Article
Language:	English
Published:	Taylor & Francis 2019
Subjects:	QA76 Computer software QC Physics TD Environmental technology. Sanitary engineering TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/27444/ http://umpir.ump.edu.my/id/eprint/27444/ http://umpir.ump.edu.my/id/eprint/27444/ http://umpir.ump.edu.my/id/eprint/27444/1/Implementation%20of%20evolutionary%20computing%20models%20for%20reference%20evapotranspiration.pdf

id	ump-27444
recordtype	eprints
spelling	ump-274442020-01-17T02:01:37Z http://umpir.ump.edu.my/id/eprint/27444/ Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions Jing, Wang Yaseen, Zaher Mundher Shamsuddin, Shahid Saggi, Mandeep Kaur Tao, H. Kisi, Ozgur Salih, Sinan Q. Al-Ansari, Nadhir Chau, Kwok-Wing QA76 Computer software QC Physics TD Environmental technology. Sanitary engineering TK Electrical engineering. Electronics Nuclear engineering 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. Taylor & Francis 2019-08-08 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/27444/1/Implementation%20of%20evolutionary%20computing%20models%20for%20reference%20evapotranspiration.pdf 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 https://doi.org/10.1080/19942060.2019.1645045 https://doi.org/10.1080/19942060.2019.1645045
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software QC Physics TD Environmental technology. Sanitary engineering TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA76 Computer software QC Physics TD Environmental technology. Sanitary engineering TK Electrical engineering. Electronics Nuclear engineering Jing, Wang Yaseen, Zaher Mundher Shamsuddin, Shahid Saggi, Mandeep Kaur Tao, H. Kisi, Ozgur Salih, Sinan Q. Al-Ansari, Nadhir Chau, Kwok-Wing Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions
description	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.
format	Article
author	Jing, Wang Yaseen, Zaher Mundher Shamsuddin, Shahid Saggi, Mandeep Kaur Tao, H. Kisi, Ozgur Salih, Sinan Q. Al-Ansari, Nadhir Chau, Kwok-Wing
author_facet	Jing, Wang Yaseen, Zaher Mundher Shamsuddin, Shahid Saggi, Mandeep Kaur Tao, H. Kisi, Ozgur Salih, Sinan Q. Al-Ansari, Nadhir Chau, Kwok-Wing
author_sort	Jing, Wang
title	Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions
title_short	Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions
title_full	Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions
title_fullStr	Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions
title_full_unstemmed	Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions
title_sort	implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions
publisher	Taylor & Francis
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/27444/ http://umpir.ump.edu.my/id/eprint/27444/ http://umpir.ump.edu.my/id/eprint/27444/ http://umpir.ump.edu.my/id/eprint/27444/1/Implementation%20of%20evolutionary%20computing%20models%20for%20reference%20evapotranspiration.pdf
first_indexed	2023-09-18T22:43:09Z
last_indexed	2023-09-18T22:43:09Z
_version_	1777417048914132992

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

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