River Flow Forecasting with Constructive Neural Network

2005 ◽  
pp. 1031-1036 ◽  
Author(s):  
Mêuser Valença ◽  
Teresa Ludermir ◽  
Anelle Valença
Keyword(s):  
Neural Network ◽  
River Flow ◽  
River Flow Forecasting

A comparison between neural-network forecasting techniques-case study: river flow forecasting

1999 ◽  
Vol 10 (2) ◽  
pp. 402-409 ◽  
Author(s):  
A.F. Atiya ◽  
S.M. El-Shoura ◽  
S.I. Shaheen ◽  
M.S. El-Sherif
Keyword(s):  
Neural Network ◽  
River Flow ◽  
River Flow Forecasting ◽  
Forecasting Techniques

scholarly journals Improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps

2012 ◽  
Vol 15 (2) ◽  
pp. 486-502 ◽  
Author(s):  
Mukesh K. Tiwari ◽  
Ki-Young Song ◽  
Chandranath Chatterjee ◽  
Madan M. Gupta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Lead Time ◽  
River Flow ◽  
Simulation Studies ◽  
River Flow Forecasting ◽  
Testing Dataset ◽  
Crucial Problem ◽  
Daily Discharge ◽  
Selection Of

Neural network (NN) models have gained much attention for river flow forecasting because of their ability to map complex non-linearities. However, the selection of appropriate length of training datasets is crucial and the uncertainty in predictions of the trained NNs with new datasets is a crucial problem. In this study, self-organising maps (SOM) are used to classify the datasets homogeneously and the performance of four types of NN models developed for daily discharge predictions – namely traditional NN, wavelet-based NN (WNN), bootstrap-based NN (BNN) and wavelet-bootstrap-based NN (WBNN) – is analysed for their applicability cluster-wise. SOM classified the training datasets into three clusters (i.e. cluster I, II and III) and the trained SOM is then used to assign testing datasets into these three clusters. Simulation studies show that the WBNN model performs better for the entire testing dataset as well as for values in clusters I and III; for cluster II the performance of BNN model is better compared with others for a 1-day lead time forecasting. Overall, it is found that the proposed methodology can enhance the accuracy and reliability of river flow forecasting.

EVALUATION OF ARTIFICIAL NEURAL NETWORK TECHNIQUES FOR RIVER FLOW FORECASTING

2007 ◽  
Vol 6 (1) ◽  
pp. 37-43 ◽  
Author(s):  
George Gabitsinashvili ◽  
Dimitri Namgaladze ◽  
Cintia Bertacchi Uvo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Flow ◽  
River Flow Forecasting ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document