Paper Details

The relationship between rainfall and runoff is one of the most complex hydrologic phenomena to comprehend dueto the tremendous spatial and temporal variability of watershed characteristics and precipitation patterns, and the number ofvariables involved in the modeling of the physical processes. As a result of these difficulties, and of a poor understanding ofthe real-world processes, empiricism can play an important role in modeling of R-R relationships. Artificial NeuralNetworks (ANNs) are typical examples of empirical models. Their ability to extract relations between inputs and outputs of aprocess, without the physics being explicitly provided to them, theoretically suits the problem of relating rainfall to runoffwell, since it is a highly nonlinear and complex problem. The goal of this investigation was to develop rainfall-runoff modelsfor the river Jhelum catchment that are capable of accurately modelling the relationships between rainfall and runoff in acatchment. Two types of ANN models viz. Back Propagation networks (BPN) and Radial Basis function (RBF) weredeveloped. The network architecture in the back propagation network was changed by changing the number of neurons in thehidden layer. The analysis of performance of the various models was carried out by statistical analysis technique .Thecomparison was based on various statistical parameters like root mean square error (RMSE) and R2.

Lateef Ahmad Dar, “OPTIMISATION OF NEURAL NETWORKS FOR RAINFALL-RUNOFF MODELING”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 11, pp. 199-203, November 2017.