Water Resources Management

, Volume 22, Issue 4, pp 423–441 | Cite as

Event-based Sediment Yield Modeling using Artificial Neural Network



In the present study, a back propagation feedforward artificial neural network (ANN) model was developed for the computation of event-based temporal variation of sediment yield from the watersheds. The training of the network was performed by using the gradient descent algorithm with automated Bayesian regularization, and different ANN structures were tried with different input patterns. The model was developed from the storm event data (i.e. rainfall intensity, runoff and sediment flow) registered over the two small watersheds and the responses were computed in terms of runoff hydrographs and sedimentographs. Selection of input variables was made by using the autocorrelation and cross-correlation analysis of the data as well as by using the concept of travel time of the watershed. Finally, the best fit ANN model with suitable combination of input variables was selected using the statistical criteria such as root mean square error (RMSE), correlation coefficient (CC) and Nash efficiency (CE), and used for the computation of runoff hydrographs and sedimentographs. Further, the relative performance of the ANN model was also evaluated by comparing the results obtained from the linear transfer function model. The error criteria viz. Nash efficiency (CE), error in peak sediment flow rate (EPS), error in time to peak (ETP) and error in total sediment yield (ESY) for the storm events were estimated for the performance evaluation of the models. Based on these criteria, ANN based model results better agreement than the linear transfer function model for the computation of runoff hydrographs and sedimentographs for both the watersheds.


Automated Bayesian Regularization ANN Event-based Runoff Sediment yield Sedimentograph Small watersheds 

List of notations and abbreviation


Target output at node i


Network output at node i


Number of observation

\( \overline{X} _{{k + 1}} \)

Weight factor at iteration (k + 1)

\( \overline{g} \)

\( = \nabla f{\left( {\overline{X} _{{_{k} }} } \right)} = \) error gradient vector


Normalized dimensionless variable


Observed value of variable


Minimum value of variable


Maximum value of variable


Output at ith hidden node


Net output at ith hidden node


Direct runoff at time t

Q(t − r)

Direct runoff at lag-r


Sediment flow at time t


Observed sediment flow


Computed sediment flow

\( \overline{S} _{{\text{O}}} \)

Mean of observed sediment flow


Sum of square error


Sum of square network weights


Objective function


Parameter of objective function


Parameter of objective function

S(t − p)

Sediment flow at lag-p


Rainfall intensity at time t

R(t − q)

Rainfall intensity at lag-q

p, q, r



Chosen step size




Nash efficiency


Error in peak sediment flow rate


Error in time to peak


Error in sediment yield


Root mean square error


Correlation coefficient


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Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Department of HydrologyIndian Institute of Technology RoorkeeUttarakhandIndia

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