Flood forecasting in similar catchments using neural networks

G. R. RAKHSHANDEHROO; M. VAGHEFI; M. M. SHAFIEE

Article Details

Flood forecasting in similar catchments using neural networks

Authors:

G. R. RAKHSHANDEHROO | M. VAGHEFI | M. M. SHAFIEE

Publisher:

TUBITAK

Journal:

Turkish Journal of Engineering and Environmental Sciences 2010 Vol.34 Issue 1, pp.57-66

Document Type:

Article

Publication Date:

12/2009

Document Language:

Turkish

Document ID (Org No.):

2000744738

Frequency:

Tri-annual

eISSN:

1303-6157

pISSN:

1300-0160

Abstract:

 

Flood forecasting is an essential requirement for solving a  wide range of scientific and/or management problems.  Neural networks  have become attractive models in many practical applications,  including flood forecasting.  In this paper, 4 similar catchments in  Iran, with high quality rainfall-runoff records, were studied.  An  artificial neural network (ANN) was built and trained as an  event-based modeling tool utilizing data from only 2 of the  catchments.  The flood forecasting ability of the model was then  evaluated for all catchments.  Results showed that during the  training, the model simulated observed peak flows very closely.  Based  on a good simulation of the response from unseen but similar  catchments, it was concluded that the model may be utilized for flood  forecasting in catchments that lie on the same cluster.  However, as  such catchments become less similar to the cluster; the simulation  error would increase accordingly.

 

Flood forecasting is an essential requirement for solving a  wide range of scientific and/or management problems.  Neural networks  have become attractive models in many practical applications,  including flood forecasting.  In this paper, 4 similar catchments in  Iran, with high quality rainfall-runoff records, were studied.  An  artificial neural network (ANN) was built and trained as an  event-based modeling tool utilizing data from only 2 of the  catchments.  The flood forecasting ability of the model was then  evaluated for all catchments.  Results showed that during the  training, the model simulated observed peak flows very closely.  Based  on a good simulation of the response from unseen but similar  catchments, it was concluded that the model may be utilized for flood  forecasting in catchments that lie on the same cluster.  However, as  such catchments become less similar to the cluster; the simulation  error would increase accordingly.

Article Details

Flood forecasting in similar catchments using neural networks

Authors: G. R. RAKHSHANDEHROO | M. VAGHEFI | M. M. SHAFIEE

Publisher: TUBITAK

Journal: Turkish Journal of Engineering and Environmental Sciences 2010 Vol.34 Issue 1, pp.57-66

Document Type: Article

Publication Date: 12/2009

Document Language: Turkish

Document ID (Org No.): 2000744738

Frequency: Tri-annual

eISSN: 1303-6157

pISSN: 1300-0160

Issues

Authors:

G. R. RAKHSHANDEHROO | M. VAGHEFI | M. M. SHAFIEE

Publisher:

TUBITAK

Journal:

Turkish Journal of Engineering and Environmental Sciences 2010 Vol.34 Issue 1, pp.57-66

Document Type:

Article

Publication Date:

12/2009

Document Language:

Turkish

Document ID (Org No.):

2000744738

Frequency:

Tri-annual

eISSN:

1303-6157

pISSN:

1300-0160