Study on GM (1, N) self-memory and neural network combined model for evaporation forecasting

Author

Duan, Haini ; Shen, Bing ; Mo, Shuhong ; Han, Haijun

Author_Institution

Key Lab. of North-West Water Resources & Ecology Environ. of Educ. Minist., Xi´´an Univ. of Technol., Xi´´an, China

Volume

2

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

636

Lastpage

639

Abstract

Hydrological elements forecasting model has been improved continuously. The variation of hydrological elements has uncertainty, in order to explain the mechanism, we need to consider more factors related. In this study, gray model (GM (1, N)) self-memory model was established to consider more related factors, and then it was combined with back-propagation (BP) neural network model. Based on this combined, the annual potential evaporation in Moyu County, Xinjiang Uygur Autonomous Region, was forecasted with satisfactory result.

Keywords

backpropagation; evaporation; grey systems; hydrological techniques; neural nets; backpropagation neural network model; evaporation forecasting; gray model; hydrological element forecasting model; neural network combined model; self-memory model; Artificial neural networks; Atmospheric modeling; Biological system modeling; Equations; Forecasting; Mathematical model; Predictive models; BP neural network; GM(1, N); annual potential evaporation; self-memory;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5583116

Filename

5583116