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学科主题: Ecology; Limnology
题名: Modeling of cyanobacterial blooms in hypereutrophic Lake Dianchi, China
作者: Hou, GX; Song, LR; Liu, JT; Xiao, BD; Liu, YD
通讯作者: Liu, YD, Chinese Acad Sci, Inst Hydrol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
关键词: NEURAL NETWORKS
刊名: JOURNAL OF FRESHWATER ECOLOGY
发表日期: 2004-12-01
卷: 19, 期:4, 页:623-629
收录类别: SCI
文章类型: Article
部门归属: Chinese Acad Sci, Inst Hydrol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
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
摘要: Compared with other approaches for modeling and predicting, artificial neural networks are more effective in describing complex and non-linear systems. The occurrence of cyanobacterial blooms has been a continuous and serious problem over the past decades in hypereutrophic Lake Dianchi. Yet, the main factor(s) initiating these blooms remain(s) unclear. During 2001-2002 at 40 sampling sites in Lake Dianchi, physicochemical parameters possibly relating to the blooms were measured. Parameters directly or indirectly relating to the cyanobacterial blooms were used as driving factors in a back-propagation network to model the concentration of chlorophyll a. According to sensitivity analysis, chemical oxygen demand was identified as a very significant environmental factor for algal growth in Lake Dianchi.
英文摘要: Compared with other approaches for modeling and predicting, artificial neural networks are more effective in describing complex and non-linear systems. The occurrence of cyanobacterial blooms has been a continuous and serious problem over the past decades in hypereutrophic Lake Dianchi. Yet, the main factor(s) initiating these blooms remain(s) unclear. During 2001-2002 at 40 sampling sites in Lake Dianchi, physicochemical parameters possibly relating to the blooms were measured. Parameters directly or indirectly relating to the cyanobacterial blooms were used as driving factors in a back-propagation network to model the concentration of chlorophyll a. According to sensitivity analysis, chemical oxygen demand was identified as a very significant environmental factor for algal growth in Lake Dianchi.
关键词[WOS]: NEURAL NETWORKS
语种: 英语
WOS记录号: WOS:000225656300013
ISSN号: 0270-5060
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ihb.ac.cn/handle/152342/9360
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 Hydrol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China

Recommended Citation:
Guoxiang Hou; Lirong Song; Jiantong Liu; Bangding Xiao; Yongding Liu.Modeling of cyanobacterial blooms in hypereutrophic Lake Dianchi, China,JOURNAL OF FRESHWATER ECOLOGY,2004,19(4):623-629
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