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This paper traces two decades of neural network rainfall-runoff and streamflow modelling, collectively termed ‘river forecasting’. The field is now firmly established and the research community involved has much to offer hydrological science. First, however, it will be necessary to converge on more objective and consistent protocols for: selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies. It is also clear that neural network river forecasting solutions will have limited appeal for operational purposes until confidence intervals can be attached to forecasts. Modular design, ensemble experiments, and hybridization with conventional hydrological models are yielding new tools for decision-making. The full potential for modelling complex hydrological systems, and for characterizing uncertainty, has yet to be realized. Further gains could also emerge from the provision of an agreed set of benchmark data sets and associated development of superior diagnostics for more rigorous intermodel evaluation. To achieve these goals will require a paradigm shift, such that the mass of individual isolated activities, focused on incremental technical refinement, is replaced by a more coordinated, problem-solving international research body.

Artificial neural networks (ANNs) have been applied within the field of hydrological modelling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in ungauged catchments. This paper uses data from the Centre for Ecology and Hydrology's Flood Estimation Handbook (FEH) to predict T-year flood events and the index flood (the median of the annual maximum series) for 850 catchments across the UK. When compared with multiple regression models, ANNs provide improved flood estimates that can be used by engineers and hydrologists.Comparisons are also made with the empirical model presented in the FEH and a preliminary study is made of the spatial distribution of ANN residuals, highlighting the influence that geographical factors have on model performance. KeywordsArtificial neural networks, flood estimation, ungauged catchments Page 2 / 39 IntroductionThe UK Flood Estimation Handbook (FEH) notes that "many flood estimation problems arise at ungauged sites for which there are no flood peak data" (Reed and Robson, 1999:12 Pirt, 1983;Post and Jakeman, 1996;Sefton and Howarth, 1998). Page 3 / 39The FEH involves the use of an index flood procedure to derive the flood frequency curve at ungauged sites. The index flood is a middle-sized flood for which the mean or median of the flood data series is typically used (Grover et al., 2002) Hall et al. (2000) used between four and twelve input catchment characteristics to predict the same two EV1 parameter outputs using data from sites in Sumatra and Java; whereas Dastorani and Wright (2001) found that seven catchment inputs were sufficient to predict the index flood for selected catchments in the UK. This paper discusses the application of ANNs to predict the index flood for a much larger sample of selected catchments in the UK. It also considers the estimation of 10-, 20-and 30-year flood event magnitudes at such sites. Given the range of record lengths available, the 20-year flood event was chosen for further discussion as it is a convenient metric that is often used for the purposes of comparison in other studies (for example, see Reynard et al., 2004).

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