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 therefore multifaceted field yet being dynamic task nature. It characterized by high stochasticity, non-linearity, non-stationarity, feature redundancy. Various artificial intelligence (AI) modeling frameworks have been introduced to solve sediment problems. The present survey designed provide updated account the latest most relevant AI-based applications systems. review established capture subsequent developments advanced AI models applied prediction. Also, several hydrological environmental aspects are identified analyzed according results produced those studies. merits constraints well-established further discussed much detail, particularly considering state-of-the art, their application-specific appraisal, some key proposed future research directions. Together with synthesis such information drive new understanding methodologies related suspended prediction, this provides vision hydrologists, water scientists, resource engineers, oceanography planners.