River flow prediction with memory-based artificial neural networks: a case study of the Dholai river basin

Abstract

Prediction of hydrologic time series has been one of the most challenging tasks in water resources management due to the non-availability of adequate data. Recently, applications of artificial neural networks (ANNs) have proved quite successful in such situation in various fields. This paper demonstrates the use of memory-based ANNs to predict daily river flows. Two different networks, namely the gamma memory neural network (GMN) and genetic algorithm-gamma memory neural network (GA-GMN) have been chosen. The best network topologies for both the ANN models are achieved with Tanh transfer function and Levenberg-Marquardt learning rule after calibrations with multiple combinations of network parameters. The selected ANN models are then used to predict the daily mean flows of Dholai (Rukmi) river in Assam, India, a sub-basin of the Barak river basin. A comparative study of both networks indicates that the GA-GMN model performed better than the GMN model. The GA-GMN model gave better results for both training and testing dataset with minimum training MSE as 0.018 and minimum testing MSE as 22.97. Hence GA-GMN model is selected as an effective tool for predicting flow features of the Dholai river.