@inproceedings{00c1c53bbd084413850b46cfa12d62e8,
title = "Application of GA optimized wavelet neural networks for carrying capacity of water resources prediction",
abstract = "The prediction of urban water demand using a small number of representative properties is fundamental in evaluating carrying capacity of water resources. Artificial neural networks (ANNs) have recently become popular tools in the prediction of urban water demand. In this paper, an iterative method which combining the strength of back-propagation (BP) in weight learning and genetic algorithms' capability of searching the satisfying solution is proposed for optimizing wavelet neural networks (WNNs). Taking the city of Hefei in China as an example, the proposed genetic algorithms optimized WNN that required a few representative properties as possible for input data is applied to predict urban water demand in the future several years. The prediction performance of the GA Optimized WNN is compared with traditional neural networks, and simulation results demonstrate the accuracy and the reliability of the prediction methodology based on the proposed model. Finally, urban water demand in Hefei, 2008-2010, is obtained which provide reference for coordinated development of socioeconomic and water resources in Hefei.",
keywords = "Carrying capacity, Genetic algorithms, Hefei, Prediction, Water resources, Wavelet neural network",
author = "Feng Lu and Jianhua Xu and Zhanyong Wang",
year = "2009",
doi = "10.1109/ESIAT.2009.59",
language = "英语",
isbn = "9780769536828",
series = "Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009",
pages = "308--311",
booktitle = "Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009",
note = "2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009 ; Conference date: 04-07-2009 Through 05-07-2009",
}