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Application of Artificial Neural Network Model for the Prediction of Suspended Sediment Load in the Large River

Abstract : Predicting the Suspended Sediment Load (SSL) of the river is a very significant and challenging task. Being a non-linearity in the SSL data, it requires a non-linear method to get predicated in absence of field measurements. In recent decades, the application of Artificial Neural Networks (ANNs) has been considered as a milestone for the prediction of different hydrological parameters. Therefore, the present study was performed to investigate the application of ANNs to estimate the total SSL, particularly in the absence of river discharge data. The predication of SSL was done for the Ganga River at a station situated in the city of Varanasi, India. A multilayer perceptron (MLP) ANNs with a back-propagation algorithm were used to predict the SSL. The reliability of models was tested using the performance criteria such as Normalized Root Mean Square Error (NRMSE), Theil’s U statistics and Correlation coefficient. Several input parameters like rainfall, SSL, River stage. River depth and change in river bed level were used on the basis of different cases and tested to predict the SSL. The results show that the model performed efficiently to predict the SSL in the Ganga River, even in the absence of daily discharge data of the river. The values of SSL were also predicted using the predicted previous time step values of SSL to simulate the model in-case of missing field values. The model was found efficient to predict the values up to 95% of accuracy in-case of missing field value of SSL.
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https://hal.archives-ouvertes.fr/hal-03365077
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Submitted on : Tuesday, October 5, 2021 - 10:22:50 AM
Last modification on : Friday, April 1, 2022 - 3:56:22 AM

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Shishir Gaur, Aryan Mishra, Anurag Gupta, Arihant Jain, Apurve Dave, et al.. Application of Artificial Neural Network Model for the Prediction of Suspended Sediment Load in the Large River. Water Resources, Springer Nature, 2021, 48 (4), pp.565 à 575. ⟨10.1134/S0097807821040163⟩. ⟨hal-03365077⟩

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