ANN-based sediment yield models for Vamsadhara river basin (India)

Abstract

Most universally accepted feed-forward error back-propagation artificial neural network models, supported by batch- and pattern-learning, daily, weekly, ten-daily and monthly sediment yield were developed for the Vamsadhara River basin of India. The fast gradient descent optimisation technique improved with variable learning rate (α) and momentum term (β) was used for optimisation. In the process of optimisation and updating of weights, criteria adopted to terminate the process of learning was selected as a per-decided high number of iteration and the other is the generalisation of model through cross-validation. In all cases of model formulation, the data were normalised with the maximum value of the variable of the series individually. The pattern-learned models were found superior to batch-learned models. High numbers of iterations adopted for model development were found to reduce the value of the objective function, but with model's over-learning and that is reflected? Unclear what is meant by an increase and decrease of the performance in calibration and cross-validation, respectively. The generalised pattern- learned models for different time scales were compared with linear transfer function models and it was found that the pattern-learned models developed with generalisation through cross-validation were superior in general, except weekly for the study area.