Assessment of runoff via precipitation using neural networks: watershed modelling for developing environment in arid region
2019
Samantaray, Sandeep | Sahoo, Abinash | Ghose, Dillip Kumar
This work describes the application of three different neural network, (i) Back propagation neural network (BPNN), (ii) Layer Recurrent Neural Network (LRNN) and (iii) Radial Basis Fewer Network (RBFN) model, to predict runoff. Here, two scenarios were considered for developing the models. Scenario 1 exclusive of evapotranspiration and Scenario 2 with evapotranspiration are considered for experiencing the impact on runoff. Performance indicators entailed Scenario 2 performed best as compared to Scenario 1. Two watersheds Loisingha, and Saintala were considered for study. In Loisingha watershed, LRNN performed best with architecture 4-3-1 following tangential sigmoid transfer function. At Saintala, both LRNN and BPNN performed in parallel with small deviation of prediction and LRNN performed best among three networks with model architecture 4-2-1 using Log-sig transfer function for predicting runoff.
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