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Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network
Hou, Guoxiang; Li, Hongbin; Recknagel, Friedrich; Song, Lirong; Song, LR, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
2006-12-01
Source PublicationJOURNAL OF FRESHWATER ECOLOGY
ISSN0270-5060
Volume21Issue:4Pages:639-647
AbstractA radial basis function neural network was employed to model the abundance of cyanobacteria. The trained network could predict the populations of two bloom forming algal taxa with high accuracy, Nostocales spp. and Anabaena spp., in the River Darling, Australia. To elucidate the population dynamics for both Nostocales spp. and Anabaena spp., sensitivity analysis was performed with the following results. Total Kjeldahl nitrogen had a very strong influence on the abundance of the two algal taxa, electrical conductivity had a very strong negative relationship with the population of the two algal species, and flow was identified as one dominant factor influencing algal blooms after a scatter plot revealed that high flow could significantly reduce the algal biomass for both Nostocales spp. and Anabaena spp. Other variables such as turbidity, color, and pH were less important in determining the abundance and succession of the algal blooms.; A radial basis function neural network was employed to model the abundance of cyanobacteria. The trained network could predict the populations of two bloom forming algal taxa with high accuracy, Nostocales spp. and Anabaena spp., in the River Darling, Australia. To elucidate the population dynamics for both Nostocales spp. and Anabaena spp., sensitivity analysis was performed with the following results. Total Kjeldahl nitrogen had a very strong influence on the abundance of the two algal taxa, electrical conductivity had a very strong negative relationship with the population of the two algal species, and flow was identified as one dominant factor influencing algal blooms after a scatter plot revealed that high flow could significantly reduce the algal biomass for both Nostocales spp. and Anabaena spp. Other variables such as turbidity, color, and pH were less important in determining the abundance and succession of the algal blooms.
SubtypeArticle
KeywordBlue-green-algae Nakdong River Cyanobacteria Prediction Blooms Murray Korea
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 AreaEcology ; Limnology
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology ; Marine & Freshwater Biology
WOS SubjectEcology ; Limnology
WOS IDWOS:000242201300011
WOS KeywordBLUE-GREEN-ALGAE ; NAKDONG RIVER ; CYANOBACTERIA ; PREDICTION ; BLOOMS ; MURRAY ; KOREA
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/152342/8776
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. Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network[J]. JOURNAL OF FRESHWATER ECOLOGY,2006,21(4):639-647.
APA Hou, Guoxiang,Li, Hongbin,Recknagel, Friedrich,Song, Lirong,&Song, LR, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China.(2006).Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network.JOURNAL OF FRESHWATER ECOLOGY,21(4),639-647.
MLA Hou, Guoxiang,et al."Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network".JOURNAL OF FRESHWATER ECOLOGY 21.4(2006):639-647.
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