KMS Chongqing Institute of Green and Intelligent Technology, CAS
| An improved hybrid ARIMA and support vector machine model for water quality prediction | |
Guo, Yishuai1,2; Wang, Guoyin2; Zhang, Xuerui2 ; Deng, Weihui2
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| 2014 | |
| 摘要 | Traditionally, the hybrid ARIMA and support vector machine model has been often used in time series forecasting. Due to the unique variability of water quality monitoring data, the hybrid model cannot easily give perfect forecasting. Therefore, this paper proposed an improved hybrid methodology that exploits the unique strength in predicting water quality time series problems. Real data sets of water quality provided by the Ministry of Environmental Protection of People’s Republic of China during 2008-2014 were used to examine the forecasting accuracy of proposed model. The results of computational tests are very promising. © Springer International Publishing Switzerland 2014. |
| 语种 | 英语 |
| DOI | 10.1007/978-3-319-11740-9_38 |
| 会议(录)名称 | 9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014
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| 页码 | 411-422 |
| 通讯作者 | Wang, Guoyin |
| 收录类别 | EI |
| 会议地点 | Shanghai, China |
| 会议日期 | October 24, 2014 - October 26, 2014 |
