Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

This paper presents the application of a modular approach for real-time streamflow forecasting that uses different system-theoretic rainfall-runoff models according to the situation characterising the forecast instant. For each forecast instant, a specific model is applied, parameterised on the basis of the data of the similar hydrological and meteorological conditions observed in the past. In particular, the hydro-meteorological conditions are here classified with a clustering technique based on Self-Organising Maps (SOM) and, in correspondence of each specific case, different feed-forward artificial neural networks issue the streamflow forecasts one to six hours ahead, for a mid-sized case study watershed. The SOM method allows a consistent identification of the different parts of the hydrograph, representing current and near-future hydrological conditions, on the basis of the most relevant information available in the forecast instant, that is, the last values of streamflow and areal-averaged rainfall. The results show that an adequate distinction of the hydro-meteorological conditions characterising the basin, hence including additional knowledge on the forthcoming dominant hydrological processes, may considerably improve the rainfall-runoff modelling performance.