DocumentCode :
2742381
Title :
A Forecast Model of Hydrologic Single Element Medium and Long-Period Based on Rough Set Theory
Author :
Sihui, Dong
Author_Institution :
Sch. of Civil & Safety Eng., Dalian Jiaotong Univ., Dalian, China
Volume :
1
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
19
Lastpage :
25
Abstract :
Based on rough set theory, this paper presents the single element medium and long-term classification forecast model which uses historical data of a hydrologic series as forecast factors. The minimal rule set, i.e., forecast pattern set, is achieved according to the principle of maximal attribute significance and rules frequency. Maximal support strength is put forward and applied to predict by using the model. The model is applied to forecast annual runoff of Dahuofang reservoir. The result indicates that the forecast model based on rough set can describe the relationship between forecast factors and forecast object efficiently and accurately. The model, which is composed of simple solution rules, is easily understood and applied.
Keywords :
forecasting theory; geophysics computing; hydrological techniques; hydrology; modelling; reservoirs; rough set theory; China; Dahuofang reservoir; annual runoff forecasting; forecast factors; forecast pattern set; hydrologic historical data; long term classification forecast model; maximal attribute significance principle; minimal rule set; rough set theory; rule frequency; single element medium forecast model; Data engineering; Extrapolation; Extraterrestrial measurements; Fuzzy reasoning; Fuzzy systems; Information analysis; Predictive models; Safety; Set theory; Weather forecasting; attribute significance; forecast model; hydrology; medium and long-term forecasts; rough set; single element; support strength;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
Type :
conf
DOI :
10.1109/FSKD.2009.356
Filename :
5358670
Link To Document :
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