Staff View: Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifaceted field yet being a dynamic task in nature. It is characterized by high stochasticity, non-linearit...

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Main Authors:	Tao, Hai, Al-Khafaji, Zainab S., Qi, Chongchong, Zounemat-Kermani, Mohammad, Kisi, Ozgur, Tiyasha, Tiyasha, Chau, Kwok Wing, Nourani, Vahid, Melesse, Assefa M., Elhakeem, Mohamed, Farooque, Aitazaz Ahsan, Nejadhashemi, A. Pouyan, Khedher, Khaled Mohamed, Alawi, Omer A., Deo, Ravinesh C., Shahid, Shamsuddin, Singh, Vijay P., Yaseen, Zaher Mundher
Format:	Article
Language:	English
Published:	Taylor and Francis Ltd. 2021
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/95590/1/ShamsuddinShahid2021_ArtificialIntelligenceModelsforSuspended.pdf http://eprints.utm.my/id/eprint/95590/ http://dx.doi.org/10.1080/19942060.2021.1984992
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id	my.utm.95590
record_format	eprints
spelling	my.utm.955902022-05-31T13:04:29Z http://eprints.utm.my/id/eprint/95590/ Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions Tao, Hai Al-Khafaji, Zainab S. Qi, Chongchong Zounemat-Kermani, Mohammad Kisi, Ozgur Tiyasha, Tiyasha Chau, Kwok Wing Nourani, Vahid Melesse, Assefa M. Elhakeem, Mohamed Farooque, Aitazaz Ahsan Nejadhashemi, A. Pouyan Khedher, Khaled Mohamed Alawi, Omer A. Deo, Ravinesh C. Shahid, Shamsuddin Singh, Vijay P. Yaseen, Zaher Mundher TA Engineering (General). Civil engineering (General) River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifaceted field yet being a dynamic task in nature. It is characterized by high stochasticity, non-linearity, non-stationarity, and feature redundancy. Various artificial intelligence (AI) modeling frameworks have been introduced to solve river sediment problems. The present survey is designed to provide an updated account of the latest and most relevant AI-based applications for modeling the sediment transport in river basin systems. The review is established to capture the subsequent developments in the advanced AI models applied for river sediment transport prediction. Also, several hydrological and environmental aspects are identified and analyzed according to the results produced in those studies. The merits and constraints of the well-established AI models are further discussed in much detail, particularly considering state-of-the art, modeling frameworks and their application-specific appraisal, and some of the key proposed future research directions. Together with the synthesis of such information to drive a new understanding of models and methodologies related to suspended river sediment prediction, this review provides a future research vision for hydrologists, water scientists, water resource engineers, oceanography and environmental planners. Taylor and Francis Ltd. 2021-01 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95590/1/ShamsuddinShahid2021_ArtificialIntelligenceModelsforSuspended.pdf Tao, Hai and Al-Khafaji, Zainab S. and Qi, Chongchong and Zounemat-Kermani, Mohammad and Kisi, Ozgur and Tiyasha, Tiyasha and Chau, Kwok Wing and Nourani, Vahid and Melesse, Assefa M. and Elhakeem, Mohamed and Farooque, Aitazaz Ahsan and Nejadhashemi, A. Pouyan and Khedher, Khaled Mohamed and Alawi, Omer A. and Deo, Ravinesh C. and Shahid, Shamsuddin and Singh, Vijay P. and Yaseen, Zaher Mundher (2021) Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions. Engineering Applications of Computational Fluid Mechanics, 15 (1). pp. 1585-1612. ISSN 1994-2060 http://dx.doi.org/10.1080/19942060.2021.1984992 DOI:10.1080/19942060.2021.1984992
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Tao, Hai Al-Khafaji, Zainab S. Qi, Chongchong Zounemat-Kermani, Mohammad Kisi, Ozgur Tiyasha, Tiyasha Chau, Kwok Wing Nourani, Vahid Melesse, Assefa M. Elhakeem, Mohamed Farooque, Aitazaz Ahsan Nejadhashemi, A. Pouyan Khedher, Khaled Mohamed Alawi, Omer A. Deo, Ravinesh C. Shahid, Shamsuddin Singh, Vijay P. Yaseen, Zaher Mundher Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
description	River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifaceted field yet being a dynamic task in nature. It is characterized by high stochasticity, non-linearity, non-stationarity, and feature redundancy. Various artificial intelligence (AI) modeling frameworks have been introduced to solve river sediment problems. The present survey is designed to provide an updated account of the latest and most relevant AI-based applications for modeling the sediment transport in river basin systems. The review is established to capture the subsequent developments in the advanced AI models applied for river sediment transport prediction. Also, several hydrological and environmental aspects are identified and analyzed according to the results produced in those studies. The merits and constraints of the well-established AI models are further discussed in much detail, particularly considering state-of-the art, modeling frameworks and their application-specific appraisal, and some of the key proposed future research directions. Together with the synthesis of such information to drive a new understanding of models and methodologies related to suspended river sediment prediction, this review provides a future research vision for hydrologists, water scientists, water resource engineers, oceanography and environmental planners.
format	Article
author	Tao, Hai Al-Khafaji, Zainab S. Qi, Chongchong Zounemat-Kermani, Mohammad Kisi, Ozgur Tiyasha, Tiyasha Chau, Kwok Wing Nourani, Vahid Melesse, Assefa M. Elhakeem, Mohamed Farooque, Aitazaz Ahsan Nejadhashemi, A. Pouyan Khedher, Khaled Mohamed Alawi, Omer A. Deo, Ravinesh C. Shahid, Shamsuddin Singh, Vijay P. Yaseen, Zaher Mundher
author_facet	Tao, Hai Al-Khafaji, Zainab S. Qi, Chongchong Zounemat-Kermani, Mohammad Kisi, Ozgur Tiyasha, Tiyasha Chau, Kwok Wing Nourani, Vahid Melesse, Assefa M. Elhakeem, Mohamed Farooque, Aitazaz Ahsan Nejadhashemi, A. Pouyan Khedher, Khaled Mohamed Alawi, Omer A. Deo, Ravinesh C. Shahid, Shamsuddin Singh, Vijay P. Yaseen, Zaher Mundher
author_sort	Tao, Hai
title	Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
title_short	Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
title_full	Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
title_fullStr	Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
title_full_unstemmed	Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
title_sort	artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
publisher	Taylor and Francis Ltd.
publishDate	2021
url	http://eprints.utm.my/id/eprint/95590/1/ShamsuddinShahid2021_ArtificialIntelligenceModelsforSuspended.pdf http://eprints.utm.my/id/eprint/95590/ http://dx.doi.org/10.1080/19942060.2021.1984992
_version_	1735386823023132672
score	13.211869

Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

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