Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
Article
Article Title | Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions |
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ERA Journal ID | 41540 |
Article Category | Article |
Authors | Tao, Hai (Author), Al-Khafaji, Zainab S. (Author), Qi, Chongchong (Author), Zounemat-Kermani, Mohammad (Author), Kisi, Ozgur (Author), Tiyasha, Tiyasha (Author), Chau, Kwok-Wing (Author), Nourani, Vahid (Author), Melesse, Assefa M. (Author), Elhakeem, Mohamed (Author), Farooque, Aitazaz Ahsan (Author), Nejadhashemi, A. Pouyan (Author), Khedher, Khaled Mohamed (Author), Alawi, Omer A. (Author), Deo, Ravinesh C. (Author), Shahid, Shamsuddin (Author), Singh, Vijay P. (Author) and Yaseen, Zaher Mundher (Author) |
Journal Title | Engineering Applications of Computational Fluid Mechanics |
Journal Citation | 15 (1), pp. 1585-1612 |
Number of Pages | 28 |
Year | 2021 |
ISSN | 1994-2060 |
1997-003X | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/19942060.2021.1984992 |
Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/19942060.2021.1984992 |
Abstract | 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. |
Keywords | advanced computer aid; sediment transport modeling; artificial intelligence models; literature review |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
410402. Environmental assessment and monitoring | |
Byline Affiliations | Baoji University of Arts and Sciences, China |
Islamic University College, Iraq | |
Central South University, China | |
Shahid Bahonar University of Kerman, Iran | |
Ilia State University, United States | |
Ton Duc Thang University, Vietnam | |
Hong Kong Polytechnic University, China | |
University of Tabriz, Iran | |
Florida International University, United States | |
University of Tennessee, United States | |
University of Prince Edward Island, Canada | |
Michigan State University, United States | |
King Khalid University, Saudi Arabia | |
University of Technology Malaysia, Malaysia | |
School of Sciences | |
Texas A&M University, United States | |
Al-Ayen University, Iraq | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q6w1v/artificial-intelligence-models-for-suspended-river-sediment-prediction-state-of-the-art-modeling-framework-appraisal-and-proposed-future-research-directions
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