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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.

Erosion of agricultural cropland has been identified as a major source of sediment accumulating in water reservoirs. Emphasis is increasingly on finding the most cost-effective ways to control soil erosion to reduce reservoir sedimentation. In this study, biophysical and economic models for the Tuttle Creek Lake watershed in northeastern Kansas were integrated to determine if implementation of alternative cropland management strategies to reduce reservoir sediment are more cost-effective than dredging. In the Tuttle Creek Lake watershed, we found that if the marginal costs of agricultural best management practices (BMP) implementation become >$6.90/t of sediment reduction, then dredging becomes the economically preferred alternative. Meeting this cost requires that BMP in the form of filter strips and no-till cultivation are implemented in a targeted, cost-effective manner, not in a random pattern of voluntary adoption that may characterize BMP adoption in some watersheds. Although reservoir dred...