Browsing Critical Infrastructure Group by Subject "Neural networks (Computer science)"

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Browsing Critical Infrastructure Group by Subject "Neural networks (Computer science)"

Now showing items 1-4 of 4

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

Bruen, Michael ; Nasr, Ahmed Elssidig ; Yang, Jianqing ; Parmentier, Benoit (University College Dublin. Departmetn of Civil Engineering, 2004-12)

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, ...
A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models

Shamseldin, Asaad Y. ; O'Connor, Kieran M. ; Nasr, Ahmed Elssidig (Taylor & Francis, 2007-10)

The performance of three artificial neural network (NN) methods for combining simulated river flows, based on three different neural network structures, are compared. These network structures are; the simple neural network ...
Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecast combination

Shamseldin, Asaad Y. ; Nasr, Ahmed Elssidig ; O'Connor, Kieran M. (European Geosciences Union, 2002-10)

The multi-layer feed-forward neural network (MLFFNN) is applied in the context of river flow forecast combination, where a number of rainfall-runoff models are used simultaneously to produce an overall combined river flow ...
Development of neuro-fuzzy models to account for temporal and spatial variations in a lumped rainfall-runoff model

Nasr, Ahmed Elssidig ; Bruen, Michael (Elsevier, 2008-02)

For many good and practical reasons, lumped rainfall-runoff models are widely used to represent a catchment‟s response to rainfall. However, they have some acknowledged limitation, some of which are addressed here using a ...