River flood forecasting with a neural network model

1999 ◽  
Vol 35 (4) ◽  
pp. 1191-1197 ◽  
Author(s):  
Marina Campolo ◽  
Paolo Andreussi ◽  
Alfredo Soldati
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flood Forecasting ◽  
River Flood

A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome

2010 ◽  
Vol 35 (3-5) ◽  
pp. 187-194 ◽  
Author(s):  
G. Napolitano ◽  
L. See ◽  
B. Calvo ◽  
F. Savi ◽  
A. Heppenstall
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Flood Forecasting ◽  
Tiber River

scholarly journals Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)

2011 ◽  
Vol 403 (3-4) ◽  
pp. 367-380 ◽  
Author(s):  
Line Kong A Siou ◽  
Anne Johannet ◽  
Valérie Borrell ◽  
Séverin Pistre
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flood Forecasting ◽  
Southern France ◽  
Selection Of

Flood Forecasting Research Based on the Chaotic BP Neural Network Model

2010 ◽  
Vol 439-440 ◽  
pp. 411-416 ◽  
Author(s):  
Chang Jun Zhu ◽  
Li Ping Wu ◽  
Sha Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Chaos Theory ◽  
Bp Neural Network ◽  
Reference Value ◽  
Flood Forecasting ◽  
Training Sample ◽  
Flow Quantity ◽  
Bp Neural Network Model

In view of the problem that the predictive results of flow quantity are not ideal for the predictive models at present. Based on the chaos identification to the flood system, chaos BP neural network model are developed combined chaos theory and BP neural netwok, flood sequences are disposed by phase-space reconstruction to be as training sample. Network structure can be determined by Matlab toolbox. The established chaos BP model is used to predict the phenomenon of peak value for Huayuankou hydrometric station in 2006. The results show that the predictive model combined chaos theory and BP neural network, has certain reference value to improve flood forecasting accuracy as a new attempt.

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