Depth-Integrated Estimation of Dissolved Oxygen in a Lake

Akkoyunlu, Atilla; Altun, Halis; Ciğizoğlu, H. Kerem

doi:10.1061/(asce)ee.1943-7870.0000376

Cited by 42 publications

(22 citation statements)

References 11 publications

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“…4 and 5 indicated that the BPNN model outperformed the MLR model in the prediction of DO content. The conclusion was consistent with that of previous research, in which the accuracy of the MLR model for predicting DO was inferior to the ANN models (Akkoyunlu et al 2011;Antanasijević et al 2013a;Wen et al 2013). The findings can be explained as follows: the correlation among water quality parameters tends to be nonlinear.…”

Section: Results Of Bpnn Modelsupporting

confidence: 92%

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Shang

Dahlgren

et al. 2017

Environ Sci Pollut Res

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Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the ruralsuburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

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Section: Results Of Bpnn Modelsupporting

confidence: 92%

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Shang

Dahlgren

et al. 2017

Environ Sci Pollut Res

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“…Heddam [32] compared the performance of a multilayer perceptron neural network (MPNL) with SVM and least square SVM (LSSVM) for the estimation of river DO concentration and showed superior performance of MPNL compared with SVM and LSSVM. Ay and Kisi [28] and Akkoyunlu et al [27] used multilayer perceptron neural networks (MLPNNs) and radial basis function neural networks (RBFNNs) to predict DO concentration in USA and Turkey, respectively, and showed their efficiency. Bayram et al [29] compared the regression and teaching-learning-based optimization approaches to estimate DO concentration in Turkey by employing temperature of air and water as predictors and indicated improvement in model performance through optimization.…”

Section: Introductionmentioning

confidence: 99%

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ehteram

Sharafati

et al. 2018

Water

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Abstract:The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.

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“…Figure 6 shows that HJV operation doesn't affect to the epilimnion layer. Akkoyunlu, et al (2011) makes clear that highest production of DO is situated in the water surface, because sunlight drives photosynthesis processes. HJV operation, operated on 50 m 3 /s for 24 hours once a month, can improve the DO in the middle layer from 2 mg/l to 2-3.5 mg/l after 3 years, and then it achieves to 3-8 mg/l after 8 years HJV operation.…”

Section: Organics and Nutrients Calibrationmentioning

confidence: 99%

Dynamic Analysis of The Hollow Jet Valve Operation For Eutrophication Control in Jatiluhur Tropical-Riverine Reservoir, Indonesia

Irianto¹,

Triweko²,

Soedjono³

2014

International Journal of Environment and Resource

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The efforts for eutrophication control have been conducted in many countries, i.e. physics, chemicals and biology. One of the alternatives is hypolimnetic withdrawal technique using hollow jet valve (HJV). So, the research objective is the application of dynamic numerical model to analyze the affect of HJV to reduce organics and nutrients pollutants in Jatiluhur tropical-riverine reservoir, Indonesia. Research methods are as follow: (1) data collections; (2) analysis of bathymetric maps; (3) dynamic numerical analysis using box volume model, assisted by WASP, and (4) calibration and simulation analyses. Results of numerical analysis shows that HJV operation operated in at 50 m 3 /s, 24 hours every month along 8 years can improve the DO, BOD, TN and TP and chlorophyll-a mainly in lacustrine zone, at Jatiluhur Reservoir. Integration programs between HJV operation and pollutant sources reduction emitted by fish-cages cultured,until 99% compared with existing condition can restore Jatiluhur Reservoir from hypereutrofic to oligotrofic statues, i.e. 3 mg/l O2; less than 0,5 mg/l BOD; 0,6 mg/l of TN; 0,05 mg/l of TP and less than 10 μg/l of Chlorophyll-a.

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Depth-Integrated Estimation of Dissolved Oxygen in a Lake

Cited by 42 publications

References 11 publications

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Dynamic Analysis of The Hollow Jet Valve Operation For Eutrophication Control in Jatiluhur Tropical-Riverine Reservoir, Indonesia

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