Modelling dissolved oxygen dynamics in coastal lagoons

Hull, Vincent; Parrella, Luisa; Falcucci, Margherita

doi:10.1016/j.ecolmodel.2007.09.023

Cited by 94 publications

(47 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are a number of studies in which neural networks are used to address water resources problems (Alvisi et al, 2006;El-shafie et al, 2008;Najah et al, 2009;Akhtar et al, 2009;Hung et al, 2009;Ming et al, 2010, Najah et al, 2010aEl-shafie et al, 2011). In water quality issues, artificial Neural Networks (ANNs) were first applied by French and Recknagel (1994) to the task of learning to predict algal blooms from water quality databases.…”

Section: Introductionmentioning

confidence: 99%

“…It predicts the water quality tendency of varieties according to the current water environment quality condition, transfer and transformation rule of the pollutants in the river basin (Najah et al, 2010). In addition, several water quality models, such as determistic and stochastic models have been developed in order to manage the best practices for conserving water quality (Hull et al, 2008;Einax et al, 1999). Most of these models are very complex and require a significant amount of field data to support the analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

Najah

El-Shafie

Karim

et al. 2011

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO 3 ) and ammonical nitrogen (NH 3 -NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009-2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO 3 ) and oxygen demand (NH 3 -NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained inCorrespondence to: A. Najah (ali najah@ymail.com) Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

Najah

El-Shafie

Karim

et al. 2011

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Several DO models such as deterministic and stochastic models have been developed in order to manage the best practices for conserving the DO in water bodies (Ansa-Ansare et al 2000;García et al 2002;Wang et al 2003;Hull et al 2008;Shukla et al 2008). Most of these models are complex and need several different input data which are not easily accessible, making it a very expensive and time-consuming process (Suen et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Wen

Fang

Diao

et al. 2012

Environ Monit Assess

View full text Add to dashboard Cite

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl . Cl − was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.

show abstract

“…We used only systems where the respondents reported ''high confidence'' in the DO values; however, given the nature of the survey, there are likely some inconsistencies among estuaries in the data collection and ''averaging'' methods. Estuary surface layer and ocean boundary DO concentrations were assumed to be saturated and were calculated from the temperature and salinity of each water body using the following formula, which is applicable over the range 21uC to 40uC and 0 to 40 g kg 21 salinity (Weiss 1970;Hull et al 2008 Model development-Modeled estuaries were assumed to be horizontally well mixed and vertically stratified in two layers due to salinity-density differences. Phytoplankton, detritus, and oxygen (PDO) were modeled for each layer based on a mass balance with river and ocean import, ocean export, and nutrient-driven production (Fig.…”

Section: Methodsmentioning

confidence: 99%

Exploring estuarine eutrophication sensitivity to nutrient loading

Evans

Scavia

2013

Limnology & Oceanography

View full text Add to dashboard Cite

The sensitivity of surface chlorophyll (Chl) and bottom water dissolved oxygen (DO) to total nitrogen (TN) load was investigated using a Bayesian-based process model fit to data from a range of estuaries. The model was used to test if the sensitivity of DO depletion to TN loads is dependent only on factors controlling the sensitivity of surface Chl, or if additional factors are important. Results indicate that separate processes control Chl and DO sensitivity, and that these sensitivities vary among estuaries. Analysis of fitted parameters across estuaries showed that Chl sensitivity to TN loading was positively correlated with water residence time, and DO sensitivity to Chl was positively correlated with relative mixing depth.

show abstract

Modelling dissolved oxygen dynamics in coastal lagoons

Cited by 94 publications

References 25 publications

Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Exploring estuarine eutrophication sensitivity to nutrient loading

Contact Info

Product

Resources

About