Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network

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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
刊名	JOURNAL OF FRESHWATER ECOLOGY
	2006-12-01
卷号	21 期号:4 页码:639-647
关键词	BLUE-GREEN-ALGAE NAKDONG RIVER CYANOBACTERIA PREDICTION BLOOMS MURRAY KOREA
ISSN号	0270-5060
通讯作者	Song, LR, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
中文摘要	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.
英文摘要	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.
学科主题	Ecology; Limnology
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
类目[WOS]	Ecology ; Limnology
研究领域[WOS]	Environmental Sciences & Ecology ; Marine & Freshwater Biology
关键词[WOS]	BLUE-GREEN-ALGAE ; NAKDONG RIVER ; CYANOBACTERIA ; PREDICTION ; BLOOMS ; MURRAY ; KOREA
收录类别	SCI
语种	英语
WOS记录号	WOS:000242201300011
公开日期	2010-10-13
内容类型	期刊论文
源URL	[http://ir.ihb.ac.cn/handle/152342/8776]
专题	水生生物研究所_中科院水生所知识产出（2009年前）_期刊论文
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
推荐引用方式 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.(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.