NDLI: A time-series forecasting approach based on KPCA-LSSVM for lake water pollution

Content Provider	IEEE Xplore Digital Library
Author	Jianjun Ni Huawei Ma Li Ren
Copyright Year	2012
Description	Author affiliation: College of Computer and Information, Hohai University, Changzhou, 213022 China (Jianjun Ni; Huawei Ma) \|\| College of Hydrology and Water Resources, Hohai University, Nanjing, 210098 China (Li Ren)
Abstract	The time-series forecasting of lake water pollution is a very important and difficult issue of any lake water pollution control system. The time-series data of lake water pollution are huge, high-dimensional and nonlinear, so the information mining of it is difficult. To realize the data mining and forecasting for time-series data of lake water pollution efficiently, an improved prediction model based on the least squares support vector machine (LSSVM) is presented in this paper. To reduce the dimension of samples, the kernel principal component analysis (KPCA) method is used to extract the feature information, which contains the principal components of samples. Then the LSSVM method is used to set up the prediction model and the parameters in this model are optimized by the genetic algorithm. Finally, the proposed prediction model is applied in water pollution time-series data forecasting experiments of Taihu Lake. The experimental results show that the proposed approach has some better performances than the general LSSVM methods, such as the good predictive accuracy and stability in the time-series forecasting of lake water pollution.
Starting Page	1044
Ending Page	1048
File Size	168706
Page Count	5
File Format	PDF
ISBN	9781467300254
e-ISBN	9781467300247
DOI	10.1109/FSKD.2012.6234207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Water pollution forecasting Kernel principal component analysis Water pollution control Lakes Predictive models Feature extraction Water pollution Support vector machine Forecasting Genetic algorithms
Content Type	Text
Resource Type	Article

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