Suspended sediment yield modelling using artificial neural networks
| dc.contributor.advisor | Devendra Kumar | |
| dc.contributor.author | Kushwaha, Daniel Prakash | |
| dc.date.accessioned | 2018-04-16T09:47:05Z | |
| dc.date.available | 2018-04-16T09:47:05Z | |
| dc.date.issued | 2016-06 | |
| dc.description.abstract | Eight artificial neural networks based models were developed to predict daily suspended sediment concentration for the Baitarani river at Anandpur and Champua stations using daily discharge and daily suspended sediment concentration. The 30 years data (June 1977 to September 2006) used in this study was divided into two sets viz. a training set (1977-1996) and a testing set (1997-2006). ANNs models were calibrated by using multilayer feedforward 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, the observed and the computed suspended sediment concentration 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 on the basis of qualitative and quantitative evaluation indicate that M-6 model with (7-5-5-1) network architecture is better than all models at Champua station and M-1 model with (2-7-7-1) network architecture is better than all models at Anandpur station. | en_US |
| dc.identifier.uri | http://krishikosh.egranth.ac.in/handle/1/5810043647 | |
| dc.keywords | sediments, models, artificial neural networks | en_US |
| dc.language.iso | en | en_US |
| dc.pages | 96 | en_US |
| dc.publisher | G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand) | en_US |
| dc.research.problem | Artifiial Neural Networks | en_US |
| dc.sub | Soil and Water Engineering | en_US |
| dc.subject | null | en_US |
| dc.theme | Agricultural Engineering | en_US |
| dc.these.type | M.Tech. | en_US |
| dc.title | Suspended sediment yield modelling using artificial neural networks | en_US |
| dc.type | Thesis | en_US |