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...
Main Authors: | , , , , , , , , , , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Taylor and Francis Ltd.
2021
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Series: | Engineering Applications of Computational Fluid Mechanics
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Subjects: | |
Online Access: | View Fulltext in Publisher View in Scopus |
LEADER | 03201nam a2200409Ia 4500 | ||
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001 | 10.1080-19942060.2021.1984992 | ||
008 | 220121s2021 CNT 000 0 und d | ||
020 | |a 19942060 (ISSN) | ||
245 | 1 | 0 | |a Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions |
260 | 0 | |b Taylor and Francis Ltd. |c 2021 | |
490 | 1 | |a Engineering Applications of Computational Fluid Mechanics | |
650 | 0 | 4 | |a Advanced computer aid |
650 | 0 | 4 | |a artificial intelligence models |
650 | 0 | 4 | |a literature review |
650 | 0 | 4 | |a sediment transport modeling |
856 | |z View Fulltext in Publisher |u https://doi.org/10.1080/19942060.2021.1984992 | ||
856 | |z View in Scopus |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118563312&doi=10.1080%2f19942060.2021.1984992&partnerID=40&md5=168909c86ea63d08d25f235de420c631 | ||
520 | 3 | |a 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. | |
700 | 1 | 0 | |a Alawi, O.A. |e author |
700 | 1 | 0 | |a Al-Khafaji, Z.S. |e author |
700 | 1 | 0 | |a Chau, K.-W. |e author |
700 | 1 | 0 | |a Deo, R.C. |e author |
700 | 1 | 0 | |a Elhakeem, M. |e author |
700 | 1 | 0 | |a Farooque, A.A. |e author |
700 | 1 | 0 | |a Khedher, K.M. |e author |
700 | 1 | 0 | |a Kisi, O. |e author |
700 | 1 | 0 | |a Melesse, A.M. |e author |
700 | 1 | 0 | |a Nourani, V. |e author |
700 | 1 | 0 | |a Pouyan Nejadhashemi, A. |e author |
700 | 1 | 0 | |a Qi, C. |e author |
700 | 1 | 0 | |a Shahid, S. |e author |
700 | 1 | 0 | |a Singh, V.P. |e author |
700 | 1 | 0 | |a Tao, H. |e author |
700 | 1 | 0 | |a Tiyasha, T. |e author |
700 | 1 | 0 | |a Yaseen, Z.M. |e author |
700 | 1 | 0 | |a Zounemat-Kermani, M. |e author |
773 | |t Engineering Applications of Computational Fluid Mechanics |