DocumentCode
3327220
Title
Prediction of Urban Water Demand Based on GA-SVM
Author
Chen, Xiaogang
Author_Institution
Digital Manuf. Technol. Lab., Huaiyin Inst. of Technol., Huaian, China
fYear
2009
fDate
6-7 June 2009
Firstpage
285
Lastpage
288
Abstract
Prediction of urban water demand is significant to urban water supply and treatment. To forecast urban water demand exactly, support vector machine optimized by genetic algorithm (GA-SVM) is proposed. Genetic algorithm(GA) is used to determine training parameters of support vector machine in GA-SVM. The experimental results indicate that the proposed GA-SVM model not only requires small training data, but also can achieve great accuracy.
Keywords
forecasting theory; genetic algorithms; learning (artificial intelligence); support vector machines; water supply; water treatment; GA-SVM model; genetic algorithm; support vector machine; training data; urban water demand forecasting; urban water demand prediction; urban water supply; urban water treatment; Artificial neural networks; Computer aided manufacturing; Demand forecasting; Fault tolerance; Genetic algorithms; Learning systems; Parallel processing; Predictive models; Support vector machines; Training data; genetic algorithm; parameter optimization; support vector machine; urban water demand;
fLanguage
English
Publisher
ieee
Conference_Titel
Future Computer and Communication, 2009. FCC '09. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3676-7
Type
conf
DOI
10.1109/FCC.2009.82
Filename
5235646
Link To Document