Title :
An Early-Warning Model of Dam Safety Based on Rough Set Theory and Support Vector Machine
Author :
Su, Huai-Zhi ; Wen, Zhi-Ping ; Gu, Chong-shi
Author_Institution :
Coll. of Water Conservancy & Hydropower Eng., Hohai Univ., Nanjing
Abstract :
There are strong non-linear and dynamic relations between dam behavior and its influence factors. The early-warning models of dam safety need to be described with non-linear function. Rough set theory is used to implement data pretreatment on dam safety monitoring. Main factors influencing dam safety are mined. An early-warning model is built with support vector machine. The proposed model can provide an effective performance of approximation and forecast for the relations between dam behavior and above mined factors. The system rule on dam behaviors can be learned and induced from the prototype observations of dam safety. The expression and parameters of early-warning model need not to be predefined
Keywords :
dams; monitoring; rough set theory; safety; structural engineering computing; support vector machines; dam behavior; dam safety early-warning model; dam safety monitoring; data pretreatment; nonlinear function; rough set theory; support vector machine; Artificial neural networks; Cybernetics; Educational institutions; Function approximation; Machine learning; Monitoring; Predictive models; Prototypes; Safety; Set theory; Support vector machines; Water conservation; Dam safety; Early-warning model; Rough set theory; Support vector machine;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258514
