Abstract
Precipitation is the only data available in most of the Indian catchments for any hydrological study. For estimation of runoff at the outlet of the study catchment from India, daily precipitation for three years recorded at five raingauge stations is the base data collected. Artificial Neural Network (ANN) a data driven technique is utilised for modelling runoff. Input variables are selected by different filter as well as wrapper approaches. Mutual information criterion is found to be useful for the ANNs for the study area. ANN architecture is optimised by selection of transfer function, training algorithm, hidden layers, hidden neurons, initial weights. For ANN weights finalisation Genetic algorithm is utilised. The performance of ANN model is validated using more than ten performance criteria. The wholesome approach helps in reinforcing the use of data driven modelling for runoff studies.
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