Runoff and Sediment Yield Modeling Using Artificial Neural Networks: Upper Siwane River, India
Publication: Journal of Hydrologic Engineering
Volume 11, Issue 1
Abstract
Accurate estimation of both runoff and sediment yield is required for proper watershed management. Artificial neural network (ANN) models were developed, to predict both runoff and sediment yield on a daily and weekly basis, for a small agricultural watershed. A total of five models were developed for predicting runoff and sediment yield, of which three models were based on a daily interval and the other two were based on a weekly interval. All five models were developed both with one and two hidden layers. Each model was developed with five different network architectures by selecting a different number of hidden neurons. The models were trained using monsoon season (June to October) data of five years (1991–1995) for different sizes of architecture, and then tested with respective rainfall and temperature data of monsoon season (June to October) of two years (1996–1997). Training was conducted using the Levenberg–Marquardt backpropagation where the input and output were presented to the neural network as a series of learning sets. Simulated surface runoff and sediment yield were compared with observed values and the minimum root-mean-square error and Nash Sutcliff efficiency (coefficient of efficiency) criteria were used for selecting the best performing model. Regression models for predicting daily and weekly runoff and sediment yield were also developed using the above training datasets, whereas these models were tested using the testing datasets. In all cases, the ANN models performed better than the linear regression based models. The ANN models with a double hidden layer were observed to be better than those with single hidden layer. Further, the ANN model prediction performance improved with increased number of hidden neurons and input variables. As a result, models considering both rainfall and temperature as input performed better than those considering rainfall alone as input. Training and testing results revealed that the models were predicting the daily and weekly runoff and sediment yield satisfactorily. Therefore, these ANN models based on simple input can be used for estimation of runoff and sediment yield, missing data, and testing the accuracy of other models.
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Published In
Journal of Hydrologic Engineering
Volume 11 • Issue 1 • January 2006
Pages: 71 - 79
Copyright
© 2006 ASCE.
History
Received: Oct 6, 2003
Accepted: Apr 26, 2005
Published online: Jan 1, 2006
Published in print: Jan 2006
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