Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network
文献类型：期刊论文
| 作者 | Hou, Guoxiang; Li, Hongbin; Recknagel, Friedrich; Song, Lirong |
| 刊名 | JOURNAL OF FRESHWATER ECOLOGY
 |
| 出版日期 | 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. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 学科主题 | Ecology; Limnology |
| 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 |
| 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. |
入库方式： OAI收割
来源：水生生物研究所
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