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学科主题: Environmental Sciences
题名: Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia
作者: Hou, Guoxiang; Li, Hongbin; Recknagel, Friedrich; Song, Lirong
通讯作者: Song, LR, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
关键词: nonstationary ; population behavior ; radial basis function ; neural network ; moving window
刊名: FRESENIUS ENVIRONMENTAL BULLETIN
发表日期: 2007
卷: 16, 期:3, 页:304-309
收录类别: SCI
文章类型: Article
部门归属: Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China; Huazhong Univ Sci & Technol, Dept Ocean Sci & Engn, Wuhan 430074, Peoples R China; Univ Adelaide, Sch Earth & Environm Sci, Adelaide, SA 5005, Australia
WOS标题词: Science & Technology ; Life Sciences & Biomedicine
类目[WOS]: Environmental Sciences
研究领域[WOS]: Environmental Sciences & Ecology
摘要: The paper demonstrates the nonstationarity of algal population behaviors by analyzing the historical populations of Nostocales spp. in the River Darling, Australia. Freshwater ecosystems are more likely to be nonstationary, instead of stationary. Nonstationarity implies that only the near past behaviors could forecast the near future for the system. However, nonstionarity was not considered seriously in previous research efforts for modeling and predicting algal population behaviors. Therefore the moving window technique was incorporated with radial basis function neural network (RBFNN) approach to deal with nonstationarity when modeling and forecasting the population behaviors of Nostocales spp. in the River Darling. The results showed that the RBFNN model could predict the timing and magnitude of algal blooms of Nostocales spp. with high accuracy. Moreover, a combined model based on individual RBFNN models was implemented, which showed superiority over the individual RBFNN models. Hence, the combined model was recommended for the modeling and forecasting of the phytoplankton populations, especially for the forecasting.
英文摘要: The paper demonstrates the nonstationarity of algal population behaviors by analyzing the historical populations of Nostocales spp. in the River Darling, Australia. Freshwater ecosystems are more likely to be nonstationary, instead of stationary. Nonstationarity implies that only the near past behaviors could forecast the near future for the system. However, nonstionarity was not considered seriously in previous research efforts for modeling and predicting algal population behaviors. Therefore the moving window technique was incorporated with radial basis function neural network (RBFNN) approach to deal with nonstationarity when modeling and forecasting the population behaviors of Nostocales spp. in the River Darling. The results showed that the RBFNN model could predict the timing and magnitude of algal blooms of Nostocales spp. with high accuracy. Moreover, a combined model based on individual RBFNN models was implemented, which showed superiority over the individual RBFNN models. Hence, the combined model was recommended for the modeling and forecasting of the phytoplankton populations, especially for the forecasting.
关键词[WOS]: MODEL ; CYANOBACTERIA ; PREDICTION
语种: 英语
WOS记录号: WOS:000245364300016
ISSN号: 1018-4619
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ihb.ac.cn/handle/152342/8666
Appears in Collections:中科院水生所知识产出(2009年前)_期刊论文

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作者单位: 1.Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
2.Huazhong Univ Sci & Technol, Dept Ocean Sci & Engn, Wuhan 430074, Peoples R China
3.Univ Adelaide, Sch Earth & Environm Sci, Adelaide, SA 5005, Australia

Recommended Citation:
Hou, Guoxiang; Li, Hongbin; Recknagel, Friedrich; Song, Lirong.Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia,FRESENIUS ENVIRONMENTAL BULLETIN,2007,16(3):304-309
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