A Neural Data Fusion Model for Hydrological Forecasting

Author

Neagoe, Victor-Emil ; Tudoran, Cristian ; Strugaru, Gabriel

Author_Institution

Politehnica Univ. of Bucharest, Bucharest

fYear

2006

fDate

24-26 July 2006

Firstpage

1

Lastpage

6

Abstract

A model of adaptive data fusion flow forecasting using concurrent neural networks (ADAFCON) is presented. It uses a fusion of rainfall and discharge data . The system consists of three backpropagation neural networks. Each neural module is trained to estimate a specific class of data dynamics: low, medium and high gradients. The decision fusion module uses a concurrential strategy. The model is applied to forecast the Piura river flow (discharge).

Keywords

backpropagation; forecasting theory; geophysics computing; hydrological techniques; rain; rivers; sensor fusion; Piura river flow forecasting; adaptive data fusion flow forecasting; backpropagation neural networks; concurrent neural networks; concurrential strategy; data dynamics; decision fusion module; hydrological forecasting; neural data fusion model; rainfall-discharge data fusion; Automation; Backpropagation; DH-HEMTs; Delta modulation; Floods; Information technology; Neural networks; Predictive models; Rivers; Technology forecasting; Concurrent Neural Networks; Data Fusion; Flow Forecasting; Hydrological Modelling;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation Congress, 2006. WAC '06. World

Conference_Location

Budapest

Print_ISBN

1-889335-33-9

Type

conf

DOI

10.1109/WAC.2006.376049

Filename

4259965