Modeling and simulation of river discharge using artificial neural networks

Abstract

The study developed multiple artificial neural network models with the aim of establishing the most suitable non-linear discharge perdition model of Ibu River. A 12-year daily discharge of River Ibu gauged near Sagamu was obtained from the Ogun-Oshun River Basin Development Authority (OORBDA), Abeokuta Nigeria to model and simulate daily discharge. The back-propagation method was used in developing the artificial neural network model. The study revealed that only three artificial neural network (ANN) models out of fifteen developed, have overall results that are satisfactory for prediction, out of these, the model with the least error was used for validation. The results obtained with ANNs based on two hidden layers for 1-day ahead are better than those obtained by models with single layers. It was concluded that the general performance of ANN models depends solely on the data used. While it was recommended that additional basin characteristics such as slope, geology, morphology and surface roughness features should be included to obtain more robust river discharge models.

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

“…Particularly for watersheds in continents with small gradients, LSTM (long short-term memory networks) have been successfully applied to predict river stages in hourly time scales with uncertainty estimates [12]. Simpler multilayer perceptron (MLP) models have produced accurate river-flow forecasts [13][14][15]. However, the previous studies considered daily to monthly forecasts, which are not applicable to the steep watersheds in Japan that have flood events over hourly time scales.…”