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COMPARISON OF MODELLING OF SUSPENDED SEDIMENT CONCENTRATION USING LINEAR AND MACHINE LEARNING METHODS
Abstract
Measured sediment concentration records are important information to support water management activities. However, for various reasons, these time series could be incomplete. This paper contains options for modelling the concentrations of suspended sediments when unmeasured periods occurs. Using of various modelling strategies for is discussed and some statistical and machine learning methods are selected for this task. The results show a significant increase in the accuracy of modelling the concentration of suspended sediments compared to the standard method, which is the rating curve. The river Danube around Bratislava (Slovakia) was selected for evaluation of the proposed methodology.
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