Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river

Abstract

This study aimed to develop novel flood forecasting models using artificial neural network (ANN) algorithms. The models were assessed and validated for the case study of an alluvial river in the Kashmir region of the Indian Himalayas-River Jhelum. In September 2014, a major flood occurred in the Kashmir Valley with peak discharge exceeding 115,000 m3/s; brought about by the multifarious interaction among atmospheric disturbances. The present study targeted the development of ANN models for flood prognosis. Thereby five neural network models were developed: Bayesian regularisation neural network, the Levenberg–Marquardt neural network, conjugate gradient neural network, scaled conjugate gradient neural network, and resilient back-propagation neural network. Levenberg–Marquardt neural network model, with the mean squared error of 0.002128 (lowest of all models) and coefficient of determination of 95.839% (highest of all models), proved to be the best model based on the statistical validation parameters. All the simulations were evaluated and curves graphed for actual versus predicted discharges for 20% of the data. These models may be used to predict floods, and take advance precautionary measures by Irrigation and Flood Control Department of the State, rather than waiting for flood hydrograph to shoot (norm as of now).