Abstract
A rainfall–runoff model that can be successfully calibrated (i.e., yielding sufficiently accurate results) using relatively short lengths of data, is desirable for any basin in general, and the basins of developing countries like India, in particular, for which scarcity of data is a major problem. An artificial neural network paradigm, known as the temporal back propagation neural network (TBP-NN), is successfully demonstrated as a monthly rainfall–runoff model. The performance of this model in a “scarce data” scenario (i.e., the effects of using reduced calibration periods on the performance) is compared with Volterra-type Functional Series Models (FSM), utilising the data of the River Lee (in the UK) and the Thuthapuzha River (in Kerala, India). The results confirm the TBP-NN model as being the most efficient of the black-box models tested for calibration periods as short as six years.
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