International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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Original Research Articles                      Volume : 6, Issue:10, October, 2017

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : /
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2017.6(10): 1386-1399

Suspended Sediment Modeling with Continuously Lagging Input Variables Using Artificial Intelligence and Physics based Models
Daniel Prakash Kushwaha* and Devendra Kumar
Department of Soil and Water Conservation Engineering, College of Technology, G. B. Pant University of Agriculture and Technology, Pantnagar-263145, Uttarakhand, India
*Corresponding author

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.

Keywords: Sediment rating curve, Multiple linear regression, Artificial neural network, Minimum description length, Akaike’s information criterion.

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How to cite this article:

Daniel Prakash Kushwaha and Devendra Kumar. 2017. Suspended Sediment Modeling with Continuously Lagging Input Variables Using Artificial Intelligence and Physics based Models.Int.J.Curr.Microbiol.App.Sci. 6(10): 1386-1399. doi:
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.