… are new to neural network hydrological modelling and as a … scope for applying neural network
modelling to hydrological … is the main area of research, neural networks are also used in …

… Figure 3 illustrates different methods that were developed for estimating the uncertainty
of neural network hydrologic models. It is evident from Fig. 3 that Monte Carlo Simulation (MCS) …

… of artificial intelligence models, generally referred to as artificial neural networks (ANNs),
within … and potential areas of application that are yet to be explored in hydrological modeling. …

… model to provide a useful horizon of forecasts is a crucial task in hydrological modeling,
several … as the networks’ outputs to evaluate and compare the performance of FFBP and EANN …

… of a hydrological model output depends on the forcing input data and the model states (eg, …
In this paper we assumed the model M is a conceptual hydrological model. The system …

… the hydrological occurrence, and therefore, neural network … their full potential as hydrological
models, they should not … being regarded as plausible hydrological models is that they …

… hydrological models using embedded neural networks (ENNs) within a conceptual
hydrological model … a set of hybrid hydrological models that employ the conceptual hydrological …

… Operational applications of hydrological models often involve the prediction of river flow for
… case, model parameters cannot be optimised with respect to locally observed hydrological …

… model becomes important. While there has been considerable interest in developing methods
for uncertainty analysis of artificial neural network (ANN) models… -based hydrologic model. …

… [9] In this case study, the SOLO network was applied to the problem of streamflow prediction,
and its performance was compared with that of four commonly used hydrological modeling …