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Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia
Hou, Guoxiang; Li, Hongbin; Recknagel, Friedrich; Song, Lirong; Song, LR, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
2007
Source PublicationFRESENIUS ENVIRONMENTAL BULLETIN
ISSN1018-4619
Volume16Issue:3Pages:304-309
AbstractThe 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.; 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.
SubtypeArticle
KeywordNonstationary Population Behavior Radial Basis Function Neural Network Moving Window
DepartmentChinese 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
Subject AreaEnvironmental Sciences
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000245364300016
WOS KeywordMODEL ; CYANOBACTERIA ; PREDICTION
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/152342/8666
Collection期刊论文
Corresponding AuthorSong, LR, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
Affiliation1.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
GB/T 7714
Hou, Guoxiang,Li, Hongbin,Recknagel, Friedrich,et al. Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia[J]. FRESENIUS ENVIRONMENTAL BULLETIN,2007,16(3):304-309.
APA Hou, Guoxiang,Li, Hongbin,Recknagel, Friedrich,Song, Lirong,&Song, LR, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China.(2007).Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia.FRESENIUS ENVIRONMENTAL BULLETIN,16(3),304-309.
MLA Hou, Guoxiang,et al."Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia".FRESENIUS ENVIRONMENTAL BULLETIN 16.3(2007):304-309.
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