Prediction of suspended sediment concentration in fluvial flows using novel hybrid deep learning model

Prediction of suspended sediment concentration in fluvial flows using novel hybrid deep learning model

Accurately predicting suspended sediment concentration (SSC) in fluvial systems is essential for environmental monitoring, flood management, and riverine engineering applications. This study introduces a novel hybrid approach for forecasting SSC by leveraging advanced deep learning algorithms. Daily...

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Bibliographic Details
Main Authors: Sadra Shadkani, Yousef Hemmatzadeh, Amirreza Pak, Soroush Abolfathi
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-08-01
Series:International Journal of Sediment Research
Subjects:
Suspended sediment
Hybrid model
Long short-term memory (LSTM)
Feed forward neural network (FFNN)
Stochastic gradient descent (SGD)
Radial basis function (RBF)
Online Access:http://www.sciencedirect.com/science/article/pii/S1001627925000241
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http://www.sciencedirect.com/science/article/pii/S1001627925000241

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