Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network
文献类型：期刊论文
| 作者 | Ye, Lin; Cai, Qinghua |
| 刊名 | JOURNAL OF FRESHWATER ECOLOGY
 |
| 出版日期 | 2009-12-01 |
| 卷号 | 24期号:4页码:609-617 |
| 关键词 | NUTRIENT LIMITATION GORGES-RESERVOIR REGULATED RIVER NAKDONG RIVER ALGAL BLOOMS DYNAMICS MODELS PREDICTION KOREA SUCCESSION |
| ISSN号 | 0270-5060 |
| 通讯作者 | Cai, QH, Wuhan Univ, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China |
| 中文摘要 | A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N. |
| 英文摘要 | A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R(2) values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R(2) values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH(3)N. |
| 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] | NUTRIENT LIMITATION ; GORGES-RESERVOIR ; REGULATED RIVER ; NAKDONG RIVER ; ALGAL BLOOMS ; DYNAMICS ; MODELS ; PREDICTION ; KOREA ; SUCCESSION |
| 收录类别 | SCI |
| 资助信息 | National Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111] |
| 语种 | 英语 |
| WOS记录号 | WOS:000271835100011 |
| 公开日期 | 2010-10-13 |
| 源URL | [http://ir.ihb.ac.cn/handle/152342/7484]  |
| 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 | Wuhan Univ, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ye, Lin,Cai, Qinghua. Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network[J]. JOURNAL OF FRESHWATER ECOLOGY,2009,24(4):609-617. |
| APA | Ye, Lin,&Cai, Qinghua.(2009).Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network.JOURNAL OF FRESHWATER ECOLOGY,24(4),609-617. |
| MLA | Ye, Lin,et al."Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network".JOURNAL OF FRESHWATER ECOLOGY 24.4(2009):609-617. |
入库方式： OAI收割
来源：水生生物研究所
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