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

Wen, Xin; Fang, Jing; Diao, Meina; Zhang, Chuanqi

doi:10.1007/s10661-012-2874-8

Cited by 89 publications

(46 citation statements)

References 52 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%

“…The appropriate neuron number in the hidden layer must be selected in advance for model construction, because too many neurons may result in overfitting problem and insufficient neurons may lead to inadequate information capture by the model (Chen and Liu 2014;Wen et al 2013). Fletcher and Goss (1993) suggested that the appropriate number of neurons in the hidden layer ranges from (a + 2n 1/2 ) to (2n + 1), where a is the output neuron number and n indicates the input neuron number.…”

Section: Results Of Bpnn Modelmentioning

confidence: 99%

“…DO is a critical water quality indicator that can be used to assess the health of aquatic ecosystems (Basant et al 2010;Wen et al 2013). Oxygen-producing processes (e.g., reaeration from the atmosphere and photosynthesis) and oxygen-consuming processes (e.g., respiration by aquatic organisms, chemical oxidation, and sediment oxygen demand) both determine DO concentrations in surface waters (Kuo et al 2007;Quinn et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…GRNN is characterized by fast learning speed, easy-to-use, and convergence to the optimal regression surface. In addition, BPNN and GRNN both have shown great potential in predicting DO concentration (Antanasijević et al 2013a(Antanasijević et al , 2014aWen et al 2013). MLR is a well-known statistical technique for establishing linear links between input and output variables and is the minimum standard for model intercomparison (Abrahart et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Results Of Bpnn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…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%

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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Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles

Ijadpanah-Saravi

Safari

Noruzi-Masir³

et al. 2017

Journal of Chemometrics

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Feasibility of applying intelligent tools in prediction and optimization of photocatalytic degradation of beta-naphthol using the titanium dioxide (TiO 2 ) nanoparticles were conducted in this study. Biphasic TiO 2 nanoparticles were synthesized using the controlled hydrolysis of TiCl 4 , and their properties were studied using the Xray diffraction and transmission electron microscopy methods. Therefore, factors affecting photocatalytic degradation of beta-naphthol including impurity concentration, catalyst content, acidity, and aeration rate were monitored and controlled. The laboratory data showed that degradation rate of beta-naphthol is a complicated nonlinear function of monitored variables. Two models including artificial network trained with particle swarm optimization (ANN-PSO) and adaptive neuro-fuzzy interference system trained with particle swarm optimization (ANFIS-PSO) were used for prediction of this system. The results showed presence of a significant relation between the real and predicted data of these 2 models. However, ANFIS-PSO can be more efficiently applied for prediction and optimization of photocatalytic behavior of TiO 2 nanoparticles as for degradation of beta-naphthol as compared to ANN-PSO. As an advantage, ANFIS eliminates the problems of fuzzy logic, such as creation of membership functions, and local minima, which should be located in design of ANN, and through PSO algorithm, it could be a very powerful tool for simulating kinds of processes.

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

Cited by 89 publications

References 52 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

Forecasting of dissolved oxygen in the river danube using neural networks

Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles

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