T-Stor
 

T-Stor >
Other Teagasc Research >
Teagasc funded research >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10197/2304

Title: Assessment of factors affecting flood forecasting accuracy and reliability. Carpe Diem Centre for Water Resources Research : Deliverable 10.3
Authors: Bruen, Michael
Nasr, Ahmed Elssidig
Yang, Jianqing
Parmentier, Benoit
Keywords: Neural network model
Flood forecasting
Rainfall prediction
SMAR model
Neural networks (Computer science)
Precipitation forecasting
Hydrologic models
Issue Date: 2-Jul-2012
Publisher: University College Dublin. Departmetn of Civil Engineering
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.
Description: 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.
URI: http://hdl.handle.net/10197/2304
Other Identifiers: http://hdl.handle.net/10197/2304
Appears in Collections:Teagasc funded research

Files in This Item:

There are no files associated with this item.


This item is protected by original copyright


View Statistics

Items in T-Stor are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! Teagasc - The Agriculture and Food Development Authority  2012  - Feedback