Title :
Prediction of Urban Water Demand Based on GA-SVM
Author_Institution :
Digital Manuf. Technol. Lab., Huaiyin Inst. of Technol., Huaian, China
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;
Conference_Titel :
Future Computer and Communication, 2009. FCC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3676-7
DOI :
10.1109/FCC.2009.82
