NDLI: Prediction model of water consumption using least square support vector machines optimized by hybrid intelligent algorithm

Content Provider	IEEE Xplore Digital Library
Author	Weilin Liu Kangning Chen Lina Liu
Copyright Year	2011
Description	Author affiliation: Research Center of Hydraulic Engineering, Nanchang Institute of Technology, China (Weilin Liu; Lina Liu) \|\| Remote Sensing Technology, Application Research Center, China Institute of Water Resources and Hydropower Research, Beijing, China (Kangning Chen)
Abstract	Accurate forecasting of water consumption has been one of the most important issues in water supply system. Because of the high generalization performance and the ability to model non-linear relationships, least square support vector machines(LS-SVM) has been successfully employed to solve water consumption forecasting problems over the past few years. However, the practical use of LS-SVM is limited due to its set of parameters to be defined by the user. For this reason, this paper presents a LS-SVM parameter optimization approach based on genetic algorithms and particle swarm optimization(hybrid intelligent algorithm). It makes use of PSO algorithm characteristics such as parallel property and the global convergence performance to avoid the local optimum, and uses the evolution idea of genetic algorithm such as crossover and mutation operations to improve the speed of searching for the global optimization. At the same time, a deterministic searching algorithm is embedded to improve its optimization performance. The application in water consumption forecasting showed, this LS-SVM optimized by hybrid intelligent algorithm achieved better forecasting result.
Starting Page	3298
Ending Page	3301
File Size	320120
Page Count	4
File Format	PDF
ISBN	9781424494361
e-ISBN	9781424494392
DOI	10.1109/MACE.2011.5987696
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Neural networks Hybrid intelligent algorithm Water consumption forecasting Least square support vector machines (LS-SVM) Forecasting Water resources Optimization Tuning Genetic algorithms
Content Type	Text
Resource Type	Article

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