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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Published in:Engineering Applications of Computational Fluid Mechanics
Main Author: Tao H.; Al-Khafaji Z.S.; Qi C.; Zounemat-Kermani M.; Kisi O.; Tiyasha T.; Chau K.-W.; Nourani V.; Melesse A.M.; Elhakeem M.; Farooque A.A.; Pouyan Nejadhashemi A.; Khedher K.M.; Alawi O.A.; Deo R.C.; Shahid S.; Singh V.P.; Yaseen Z.M.
Format: Review
Language:English
Published: Taylor and Francis Ltd. 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118563312&doi=10.1080%2f19942060.2021.1984992&partnerID=40&md5=168909c86ea63d08d25f235de420c631
id 2-s2.0-85118563312
spelling 2-s2.0-85118563312
Tao H.; Al-Khafaji Z.S.; Qi C.; Zounemat-Kermani M.; Kisi O.; Tiyasha T.; Chau K.-W.; Nourani V.; Melesse A.M.; Elhakeem M.; Farooque A.A.; Pouyan Nejadhashemi A.; Khedher K.M.; Alawi O.A.; Deo R.C.; Shahid S.; Singh V.P.; Yaseen Z.M.
Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
2021
Engineering Applications of Computational Fluid Mechanics
15
1
10.1080/19942060.2021.1984992
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118563312&doi=10.1080%2f19942060.2021.1984992&partnerID=40&md5=168909c86ea63d08d25f235de420c631
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. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Taylor and Francis Ltd.
19942060
English
Review
All Open Access; Gold Open Access
author Tao H.; Al-Khafaji Z.S.; Qi C.; Zounemat-Kermani M.; Kisi O.; Tiyasha T.; Chau K.-W.; Nourani V.; Melesse A.M.; Elhakeem M.; Farooque A.A.; Pouyan Nejadhashemi A.; Khedher K.M.; Alawi O.A.; Deo R.C.; Shahid S.; Singh V.P.; Yaseen Z.M.
spellingShingle Tao H.; Al-Khafaji Z.S.; Qi C.; Zounemat-Kermani M.; Kisi O.; Tiyasha T.; Chau K.-W.; Nourani V.; Melesse A.M.; Elhakeem M.; Farooque A.A.; Pouyan Nejadhashemi A.; Khedher K.M.; Alawi O.A.; Deo R.C.; Shahid S.; Singh V.P.; Yaseen Z.M.
Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
author_facet Tao H.; Al-Khafaji Z.S.; Qi C.; Zounemat-Kermani M.; Kisi O.; Tiyasha T.; Chau K.-W.; Nourani V.; Melesse A.M.; Elhakeem M.; Farooque A.A.; Pouyan Nejadhashemi A.; Khedher K.M.; Alawi O.A.; Deo R.C.; Shahid S.; Singh V.P.; Yaseen Z.M.
author_sort Tao H.; Al-Khafaji Z.S.; Qi C.; Zounemat-Kermani M.; Kisi O.; Tiyasha T.; Chau K.-W.; Nourani V.; Melesse A.M.; Elhakeem M.; Farooque A.A.; Pouyan Nejadhashemi A.; Khedher K.M.; Alawi O.A.; Deo R.C.; Shahid S.; Singh V.P.; Yaseen Z.M.
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
publishDate 2021
container_title Engineering Applications of Computational Fluid Mechanics
container_volume 15
container_issue 1
doi_str_mv 10.1080/19942060.2021.1984992
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118563312&doi=10.1080%2f19942060.2021.1984992&partnerID=40&md5=168909c86ea63d08d25f235de420c631
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. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
publisher Taylor and Francis Ltd.
issn 19942060
language English
format Review
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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