TY - JOUR
T1 - Artificial intelligence models for suspended river sediment prediction
T2 - state-of-the art, modeling framework appraisal, and proposed future research directions
AU - Tao, Hai
AU - Al-Khafaji, Zainab S.
AU - Qi, Chongchong
AU - Zounemat-Kermani, Mohammad
AU - Kisi, Ozgur
AU - Tiyasha, Tiyasha
AU - Chau, Kwok Wing
AU - Nourani, Vahid
AU - Melesse, Assefa M.
AU - Elhakeem, Mohamed
AU - Farooque, Aitazaz Ahsan
AU - Pouyan Nejadhashemi, A.
AU - Khedher, Khaled Mohamed
AU - Alawi, Omer A.
AU - Deo, Ravinesh C.
AU - Shahid, Shamsuddin
AU - Singh, Vijay P.
AU - Yaseen, Zaher Mundher
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
KW - Advanced computer aid
KW - artificial intelligence models
KW - literature review
KW - sediment transport modeling
UR - http://www.scopus.com/inward/record.url?scp=85118563312&partnerID=8YFLogxK
U2 - 10.1080/19942060.2021.1984992
DO - 10.1080/19942060.2021.1984992
M3 - Review article
AN - SCOPUS:85118563312
SN - 1994-2060
VL - 15
SP - 1585
EP - 1612
JO - Engineering Applications of Computational Fluid Mechanics
JF - Engineering Applications of Computational Fluid Mechanics
IS - 1
ER -