Performance of optimum neural network in rainfall–runoff modeling over a river basin

An optimum architecture backpropagation neural network for rainfall–runoff modeling is rarely visible in terms of number of input vector, hidden neurons, learning rate, momentum factor, number of epochs for training up to global minima, and error minimized at global minima. These parameters are optimized to forecast total rainfall–runoff from Basantpur station of Mahanadi river basin of central India situated at 80°28′ and 86°43′E longitudes as well as 19°8′ and 23°32′N latitudes, with a breadth 587 and 400 km as case study. Intended for four optimum models for the month of July (5, 2, 0.31, 0.72, 5,000,000, 0.001787722), August (8, 2, 0.21, 0.71, 7,500,000, 0.004556357), September (3, 2, 0.31 0.89, 20,000,000, 0.007621867), and October (8, 2, 0.24, 0.85, 7,500,000, 0.001587539) are experimentally constructed. The performance of these models with reference to correlation coefficient between real and simulated models during training and testing time is found excellent except for model of September. Wherein, correlation coefficient as 0.95, 0.71, 0.94 for the training period and 0.86, 0.53, 0.88 for the testing period are found for the corresponding models of July, August, and October, respectively. It is concluded that the optimum backpropagation neural networks are successful in rainfall–runoff modeling over a river basin for forecast of total rainfall discharge. While, for any application an optimum architecture is pre-requisite to find most favorable performance; however, it does not indicate that model will perform well has been seen in the case of September. These facts and results are discussed in this research article.