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Monthly Reservoir Inflow Modeling Using Time Lagged Recurrent Networks

Alka S. Kote, V. Jothiprakash


This study evaluates the performance of two modeling approaches for an intermittent reservoir in series. In the first approach, artificial neural network (ANN) model has been developed and applied to the observed time series of Yedgaon reservoir in Upper Bhima River Basin, India. The ANN model results are compared with conventional univariate auto regressive, auto regressive moving average, and auto regressive integrated moving average models. The number of input nodes in ANN model is varied and found that all the training algorithms performed better with four nodes. Back propagation through time algorithm, conjugate gradient algorithm and radial basis function are employed with combination of activation functions such as sigmoid, hyperbolic tangent, step, quick prop, delta rule delta to achieve the best training network. Principal statistic criteria of the predicted inflow time series are compared with observed inflows. Eleven goodnessof- fit measures are also employed to select the best model in each case. The prediction of inflow using time lagged recurrent network with time delay neural network has resulted in a better scenario. However the high and low inflows are not captured well by the ANN network. In the second approach, the network is modified and adopted for predicting seasonal (monsoon) reservoir inflows and the results show accurate mapping of high and low inflows.


Multi-layer perceptron, stochastic models, reservoir inflow, time series, transformation, seasonal.

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