Application of artificial neural networks to predict peak flow of Surma River in Sylhet Zone of Bangladesh

Abstract

River flow analysis and prediction is an important task in water resources planning, particularly for a disaster-prone agricultural country like Bangladesh. The present study used two ANN models namely radial basis function (RBF) and multi-layer perceptron (MLP) to analyse Surma River flow and estimate its peak flow concentration based on five input parameters. The performances of selected models were measured using the correlation coefficient (R), mean absolute error (MAE) and model efficiency (EFF%). However, RBF network model performed better than MLP network model with high model efficiency (99.55%), low mean squared errors (38.60) and high correlation coefficient (0.996), where the optimum number of neurons was 18 for RBF and 22 for MPL network. Moreover, the proposed ANN models could be used successfully in estimating the peak-flow of the Surma River, which would facilitate water resources management policy of this region.