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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Autores principales:	Hai Tao, Zainab S. Al-Khafaji, Chongchong Qi, Mohammad Zounemat-Kermani, Ozgur Kisi, Tiyasha Tiyasha, Kwok-Wing Chau, Vahid Nourani, Assefa M. Melesse, Mohamed Elhakeem, Aitazaz Ahsan Farooque, A. Pouyan Nejadhashemi, Khaled Mohamed Khedher, Omer A. Alawi, Ravinesh C. Deo, Shamsuddin Shahid, Vijay P. Singh, Zaher Mundher Yaseen
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	advanced computer aid sediment transport modeling artificial intelligence models literature review Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/6d41e13b728b4aa988af3da02339555a
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spelling	oai:doaj.org-article:6d41e13b728b4aa988af3da02339555a2021-11-04T15:00:43ZArtificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions1994-20601997-003X10.1080/19942060.2021.1984992https://doaj.org/article/6d41e13b728b4aa988af3da02339555a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/19942060.2021.1984992https://doaj.org/toc/1994-2060https://doaj.org/toc/1997-003XRiver 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.Hai TaoZainab S. Al-KhafajiChongchong QiMohammad Zounemat-KermaniOzgur KisiTiyasha TiyashaKwok-Wing ChauVahid NouraniAssefa M. MelesseMohamed ElhakeemAitazaz Ahsan FarooqueA. Pouyan NejadhashemiKhaled Mohamed KhedherOmer A. AlawiRavinesh C. DeoShamsuddin ShahidVijay P. SinghZaher Mundher YaseenTaylor & Francis Grouparticleadvanced computer aidsediment transport modelingartificial intelligence modelsliterature reviewEngineering (General). Civil engineering (General)TA1-2040ENEngineering Applications of Computational Fluid Mechanics, Vol 15, Iss 1, Pp 1585-1612 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	advanced computer aid sediment transport modeling artificial intelligence models literature review Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	advanced computer aid sediment transport modeling artificial intelligence models literature review Engineering (General). Civil engineering (General) TA1-2040 Hai Tao Zainab S. Al-Khafaji Chongchong Qi Mohammad Zounemat-Kermani Ozgur Kisi Tiyasha Tiyasha Kwok-Wing Chau Vahid Nourani Assefa M. Melesse Mohamed Elhakeem Aitazaz Ahsan Farooque A. Pouyan Nejadhashemi Khaled Mohamed Khedher Omer A. Alawi Ravinesh C. Deo Shamsuddin Shahid Vijay P. Singh Zaher Mundher Yaseen 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	Hai Tao Zainab S. Al-Khafaji Chongchong Qi Mohammad Zounemat-Kermani Ozgur Kisi Tiyasha Tiyasha Kwok-Wing Chau Vahid Nourani Assefa M. Melesse Mohamed Elhakeem Aitazaz Ahsan Farooque A. Pouyan Nejadhashemi Khaled Mohamed Khedher Omer A. Alawi Ravinesh C. Deo Shamsuddin Shahid Vijay P. Singh Zaher Mundher Yaseen
author_facet	Hai Tao Zainab S. Al-Khafaji Chongchong Qi Mohammad Zounemat-Kermani Ozgur Kisi Tiyasha Tiyasha Kwok-Wing Chau Vahid Nourani Assefa M. Melesse Mohamed Elhakeem Aitazaz Ahsan Farooque A. Pouyan Nejadhashemi Khaled Mohamed Khedher Omer A. Alawi Ravinesh C. Deo Shamsuddin Shahid Vijay P. Singh Zaher Mundher Yaseen
author_sort	Hai Tao
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 & Francis Group
publishDate	2021
url	https://doaj.org/article/6d41e13b728b4aa988af3da02339555a
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Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

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