HIGHLIGHTS
SUMMARY
The effectiveness of artificial neural_network (ANN) models in hydrological forecasting often exceeds the effectiveness of traditional conceptual or mathematical models based on the modelling of complex hydrological processes. In the development of flow forecasts and the assessment of their quality, the conventional linear autoregressive relationship (AR), ANN models (e_g, three-layer feedforward neural_network), recursive neural_networks (RNN)), and a number of hybrid models are used. The ANN models have also been used to model rainfall-runoff, riverflow, and flood forecasting, among others, by Imrie et_al for selected catchments in the UK; by Kim and Barros . . .
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