Abstract:Suspended sediment load (SSL) prediction study is critical to water resource management. This paper presents studies related to the prediction of SSL using machine learning (ML) algorithms over the last 13 years. This research gives a survey of current studies that are used machine learning techniques to predict sediment load on several rivers in different reign. Also, it aims to find a performance model to predict the SSL. This is done by making comparisons between several studies that used machine learning t… Show more
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