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Assessment of factors affecting flood forecasting accuracy and reliability. Carpe Diem Centre for Water Resources Research : Deliverable 10.3

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Keywords
Neural network model, Flood forecasting, Rainfall prediction, SMAR model, Neural networks (Computer science), Precipitation forecasting, Hydrologic models
Date
2012-07-02
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Teagasc funded research
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URI
http://hdl.handle.net/10197/2304
Citation
Abstract
In Deliverable 10.1, a optimal methodology for combining precipitation information from raingauges, radar and NWP models (in this case HIRLAM) was described. It was based on an artificial neural network combination model, fitted to historic data, and operating on one-dimensional time-series of discharges. In this report, this new methodology is tested by applying it to (i) a rural catchment (Dargle)and (ii) a small urban catchment (CityWest). The results are compared with measured discharge series in both cases. Various measures of performance, applied to both the entire discharge series and also to the peaks-only are reported for various combinations of lead-time, spatial resolution and numbers of neurons in the hidden layer of the ANN model.
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