A comparison of machine learning models for suspended sediment load classification

نویسندگان

چکیده

The suspended sediment load (SSL) is one of the major hydrological processes affecting sustainability river planning and management. Moreover, sediments have a significant impact on dam operation reservoir capacity. To this end, reliable applicable models are required to compute classify SSL in rivers. application machine learning has become common solve complex problems such as modeling. present research investigated ability several data. This investigation aims explore new version classifiers for classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron k-nearest neighbors been used values divided into multiple discrete ranges, where each range can be considered category or class. study illustrates two different scenarios related number categories, which five 10 with time scales, daily weekly. performance proposed was evaluated by statistical indicators. Overall, achieved excellent data under various scenarios.

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ژورنال

عنوان ژورنال: Engineering Applications of Computational Fluid Mechanics

سال: 2022

ISSN: ['1997-003X', '1994-2060']

DOI: https://doi.org/10.1080/19942060.2022.2073565