Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FSKD.2012.6234207 | FSKD |
Keywords | Field | DocType |
water pollution control,forecasting theory,feature extraction,water pollution forecasting,time-series forecasting approach,least squares support vector machine,support vector machine,least squares approximations,genetic algorithm,genetic algorithms,data mining,kpca-lssvm,information mining,principal component analysis,kernel principal component analysis,prediction model,time series,support vector machines,lake water pollution control system,time series data,control system,forecasting,time series forecasting,water pollution,predictive models,principal component | Time series,Data mining,Computer science,Kernel principal component analysis,Water pollution,Artificial intelligence,Genetic algorithm,Least squares support vector machine,Pattern recognition,Support vector machine,Feature extraction,Machine learning,Principal component analysis | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4673-0025-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjun Ni | 1 | 48 | 5.70 |
| Huawei Ma | 2 | 4 | 0.77 |
| Li Ren | 3 | 0 | 1.69 |