Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network

Jeong, Kwang‐Seuk; Joo, Gea‐Jae; Kim, Hyun Woo; Ha, Kyong; Recknagel, Friedrich

doi:10.1016/s0304-3800(01)00300-3

Cited by 92 publications

(48 citation statements)

References 11 publications

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“…Many mathematical and computational techniques have been applied to elucidate and predict the dynamics of ecosystem and the driving forces (Jorgensen and Nielsen 1994, Straskraba 1994. Most phytoplankton bloom forecasting work has been based on weekly or biweekly (Jeong et al 2001, Lee et al 2003, or even monthly data (Hou et al 2004). Forecasting based on daily observation is seldom reported.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Daily ChlorophyllaConcentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

Cai

2009

Journal of Freshwater Ecology

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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~ values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R~ 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.

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Section: Discussionmentioning

confidence: 99%

“…1). This approach had been successful for predicting time-series dynamics in some aquatic ecosystems (Jeong et al 2001, Walter et al 2001). The RANN is a type of the neural network with a feedback from the hidden layer or the output layer as the recurrent nodes to the input layer (Pineda 1987, Elman 1990, Seker et al 2003.…”

Section: Artijicial Neural Networkmentioning

confidence: 99%

Forecasting Daily ChlorophyllaConcentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

Cai

2009

Journal of Freshwater Ecology

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“…Many researches have applied sensitivity analysis approaches to determine the impact of input variables on output (Goh, 1995;Lek et al, 1996;Siginer, 1997;Dimopoulos et al, 1999;Jeong et al, 2001). In our study, the sensitivity analysis approach used is similar to the one described in Zar (1984) and Jeong et al (2001).…”

Section: 4mentioning

confidence: 99%

Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Hou

Feng

et al. 2007

Ecological Informatics

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“…These include artificial neural networks (Recknagel et al 1997(Recknagel et al , 2002Yabunaka et al 1997;Maier et al 1998;Scardi & Harding 1999;Karul et al 2000;Jeong et al 2001;Scardi 2001;Wei et al 2001;Lee et al 2003), evolutionary based techniques (Bobbin & Recknagel 2001;Recknagel et al 2002;Jeong et al 2003;Chau 2005;Muttil & Lee 2005), fuzzy and neuro-fuzzy techniques (Maier et al 2001;Chen & Mynett 2003), and so on. DM techniques with the goal of "description" have also been used, but to a lesser extent.…”

Section: Introductionmentioning

confidence: 99%

Data mining and multivariate statistical analysis for ecological system in coastal waters

Praus

Praks

2007

Journal of Hydroinformatics

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In this study, data mining using box plots and multivariate statistical analysis using factor analysis are employed for a spatio-temporal analysis of coastal water quality data from Tolo Harbour, Hong Kong. The analysis of box plots reveals pronounced spatial heterogeneity of the parameters studied. The spatial analysis clearly shows monitoring station TM2 in the Harbour Subzone to be most susceptible to eutrophication with the highest nutrient and algal biomass concentrations. The factor analysis brings to light dominant parameters to the ecological system under the coastal marine environment. The temporal analysis confirms the considerable decline in nutrient levels in recent years. In spite of this decline, the factor analysis indicates that nutrient processes play an important role even in recent years, suggesting an adequate supply of nutrients. It seems that they are being released from sources other than known point sources, possibly from nutrients accumulated in the sediments, necessitating steps to be undertaken for their control also. This study demonstrates the use of data mining techniques in the ecological system in Tolo Harbour.

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Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network

Cited by 92 publications

References 11 publications

Forecasting Daily ChlorophyllaConcentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

Forecasting Daily ChlorophyllaConcentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Data mining and multivariate statistical analysis for ecological system in coastal waters

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