Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

Hai Tao, Zainab S. Al-Khafaji, Chongchong Qi, Mohammad Zounemat-Kermani, Ozgur Kisi, Tiyasha Tiyasha, Kwok Wing Chau, Vahid Nourani, Assefa M. Melesse, Mohamed Elhakeem, Aitazaz Ahsan Farooque, A. Pouyan Nejadhashemi, Khaled Mohamed Khedher, Omer A. Alawi, Ravinesh C. Deo, Shamsuddin Shahid, Vijay P. Singh, Zaher Mundher Yaseen

Research output: Journal article publicationReview articleAcademic researchpeer-review


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.

Original languageEnglish
Pages (from-to)1585-1612
Number of pages28
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 2021


  • Advanced computer aid
  • artificial intelligence models
  • literature review
  • sediment transport modeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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