National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Artificial neural network (ANN) models were developed to predict daily suspended sediment concentration (SSC) for the Baitarani River at Champua station using daily SSC and daily discharge. ANN models were calibrated by using multilayer feed forward back propagation neural networks with sigmoid activation function and Levenberg-Marquardt (L-M) learning algorithm. The performance of the developed models was evaluated qualitatively and quantitatively. In qualitative evaluation of models, observed suspended sediment concentration (OSCC) and computed suspended sediment concentration (CSSC) were compared using sediment hydrographs and scatter plots during testing period. Akaike’s information criterion (AIC), correlation coefficient (r), mean square error (MSE), root mean square error (RMSE), minimum description length (MDL), coefficient of efficiency (CE) and normalized mean square error (NMSE) indices were used for quantitative performance evaluation of the models. Results indicate that M-6 model with (7-5-5-1) network architecture is better than all models and it was also found that ANN based model is better than physics based models such as sediment rating curve and multiple linear regression for the prediction of SSC.
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