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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
Ye, Lin; Cai, Qinghua; Cai, QH, Wuhan Univ, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
2009-12-01
Source PublicationJOURNAL OF FRESHWATER ECOLOGY
ISSN0270-5060
Volume24Issue:4Pages:609-617
AbstractA 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.; 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.
SubtypeArticle
KeywordNutrient Limitation Gorges-reservoir Regulated River Nakdong River Algal Blooms Dynamics Models Prediction Korea Succession
Department[Ye, Lin; Cai, Qinghua] Wuhan Univ, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
Subject AreaEcology ; Limnology
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Funding OrganizationNational Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111] ; National Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111] ; National Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111] ; National Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111]
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology ; Marine & Freshwater Biology
WOS SubjectEcology ; Limnology
WOS IDWOS:000271835100011
WOS KeywordNUTRIENT LIMITATION ; GORGES-RESERVOIR ; REGULATED RIVER ; NAKDONG RIVER ; ALGAL BLOOMS ; DYNAMICS ; MODELS ; PREDICTION ; KOREA ; SUCCESSION
Funding OrganizationNational Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111] ; National Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111] ; National Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111] ; National Natural Science Foundation of China [40671197]; CAS [KZCX2-YW-427, KSCX2-SW-111]
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Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/152342/7484
Collection期刊论文
Corresponding AuthorCai, QH, Wuhan Univ, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
AffiliationWuhan Univ, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
Recommended Citation
GB/T 7714
Ye, Lin,Cai, Qinghua,Cai, QH, Wuhan Univ, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China. 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,&Cai, QH, Wuhan Univ, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China.(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.
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