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...
Published in: | Engineering Applications of Computational Fluid Mechanics |
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Format: | Review |
Language: | English |
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Taylor and Francis Ltd.
2021
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Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118563312&doi=10.1080%2f19942060.2021.1984992&partnerID=40&md5=168909c86ea63d08d25f235de420c631 |
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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 |
_version_ |
1812871799284695040 |