Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Doğan, Emrah; Şengörür, Bülent; Köklü, Rabia

doi:10.1016/j.jenvman.2008.06.004

Cited by 188 publications

(85 citation statements)

References 9 publications

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“…They are capable of providing a neuron computing approach to solve complex problems. In the last decade, ANNs have been widely successfully applied to various water resources problems, such as hydrological processes (Nayak et al 2004;Sahoo et al 2005;Dastorani et al 2010;Guo et al 2011;Wu and Chau 2011;Senkal et al 2012), water resources management (Kralisch et al 2003;Sreekanth and Datta 2010), groundwater problems (Daliakopoulos et al 2005;Dixon 2005;Garcia and Shigidi 2006;Nayak et al 2006;Ghose et al 2010;Banerjee et al 2011), and water quality (Ha and Stenstrom 2003;Kuo et al 2006;Anctil et al 2009;da Costa et al 2009;Dogan et al 2009;Chang et al 2010;He et al 2011). ANNs also have been used for modeling and forecasting DO (Kuo et al 2007;Singh et al 2009;Ranković et al 2010;Najah et al 2011).…”

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

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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.

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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

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“…In this study, sensitivity analysis was carried out by constructing 11 SVM models each use single water quality parameter as input variable (Dogan et al 2009). The effects of each parameter were assessed on the basis of the MSE during the test stage, and the results are shown in Fig.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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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“…Each node in the input and inner layers receives input values, processes it, and passes it to the next layer. This process is conducted by weights [10], meaning that the hidden layer sums the weighted inputs and own bias value and uses the own transfer function to create an output value. Typical transfer functions are the linear, the sigmoid or the hyperbolic tangent function [12].…”

Section: Back-propagation Neural Networkmentioning

confidence: 99%

“…In most research, the simply prediction of the concentration of dissolved oxygen was the aim [1,3,4,5,6,7,8,9], while in a number of studies the prediction of biological oxygen demand (BOD) was the purpose [2,7,10] and, very rarely, models were applied to the estimation of chemical oxygen demand (COD) [7,11]. MLP was applied by Rankovic et al [3] for the modelling of DO in a reservoir, in Serbia, and in their next study [8] an adaptive network-based fuzzy inference system (ANFIS) model was used on the same dataset, but with fewer input variables.…”

Section: Introductionmentioning

confidence: 99%

“…Basant et al [2] predicted the DO and BOD in the Gomti River, India, using two models (partial least squares regression and MLP), and the performance of the MLP was better. MLP was developed by Dogan et al [10] to predict the BOD in the Melen River, Turkey, and the COD was found to be more effective on the BOD estimation. The MLP with the Bayesian regularization training algorithm was successfully utilized by Wen et al [4] to simulate the DO concentrations in the Heihe River, China, where the most effective inputs were determined as pH, NO3-N and NH4-N. Two applied models (MLR and GRNN) were compared by Heddam [9] and it was found that the best fit was obtained using GRNN model in prediction of DO in the Upper Klamath River, USA.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting of dissolved oxygen in the river danube using neural networks

Csábrági¹,

Molnár²,

Tanos³

et al. 2015

HAE

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The Danube is the second-largest river in Europe and the conservation of its water quality is very important because it influences the lives of millions people. The aim of this research is to predict one of the most important water quality parameters, dissolved oxygen, with the help of water pH, runoff, water temperature and electrical conductivity data. Multivariate Linear Regression (MLR), Back-propagation Neural Networks (BPNN) and General Regression Neural Networks (GRNN) were applied and their performances compared in this study. The most accurate prediction proved to be GRNN. This paper describes the influence of single input parameters on the prediction.

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Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Cited by 188 publications

References 9 publications

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

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

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

Forecasting of dissolved oxygen in the river danube using neural networks

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