A neural network-based approach for calculating dissolved oxygen profiles in reservoirs

Soyupak, Selçuk; Karaer, Feza; Gürbüz, Hasan; Kivrak, Ersin; Sentürk, Engin; Yazici, Ali

doi:10.1007/s00521-003-0378-8

Cited by 45 publications

(21 citation statements)

References 12 publications

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“…For further assessment, the proposed models were compared with the results reported in the literature. Soyupak et al (2003) employed the ANN modelling approach to calculate the pseudo steady state time and space dependent DO concentrations in three different reservoirs, with entirely different properties. The correlation coefficients between neural network estimates and field measurements were higher than 0.95.…”

Section: Performance Of the Proposed Scenariosmentioning

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.

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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 %.

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Section: Performance Of the Proposed Scenariosmentioning

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.

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“…Bateni et al (2007) have used ANNs and ANFIS models to estimate the equilibrium and time-dependent scour depth with numerous reliable database. ANNs have been used intensively in the development of a reservoir water quality simulation model (Soyupak et al 2003;Chaves and Kojiri 2007). Palani et al (2008) used an ANN model to predict and forecast temperature, salinity, DO and Chl-a in Singapore coastal.…”

Section: Introductionmentioning

confidence: 99%

Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models

Emamgholizadeh

Kashi

Marofpoor

et al. 2013

Int. J. Environ. Sci. Technol.

143

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This paper describes the application of multilayer perceptron (MLP), radial basis network and adaptive neuro-fuzzy inference system (ANFIS) models for computing dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) levels in the Karoon River (Iran). Nine input water quality variables including EC, PH, Ca, Mg, Na, Turbidity, PO 4 , NO 3 and NO 2 , which were measured in the river water, were employed for the models. The performance of these models was assessed by the coefficient of determination R 2 , root mean square error and mean absolute error. The results showed that the computed values of DO, BOD and COD using both the artificial neural network and ANFIS models were in close agreement with their respective measured values in the river water. MLP was also better than other models in predicting water quality variables. Finally, the sensitive analysis was done to determine the relative importance and contribution of the input variables. The results showed that the phosphate was the most effective parameters on DO, BOD and COD.

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“…The prediction ability of ANFIS model is nearly similar to (Soyupak et al, 2003;Zhao, Nan, Cui, & Guo, 2007;Akpomie et al, 2016). Akpomie et al (2016) found a correlation coefficient of 0.86 for Cd concentration.…”

Section: Resultsmentioning

confidence: 57%

“…Several authors studied various AI techniques in environmental modelling, water quality monitoring and assessment, predicting the concentrations of heavy metals and other quality parameters, and estimation and forecasting in climatic sciences (Soyupak et al, 2003;Altunkaynak, Özger, & Çakmakcı, 2005;Kisi, 2005;Ocampo-Duque, Ferré-Huguet, Domingo, & Schuhmacher, 2006;Sengorur, Dogan, Koklu, & Samandar, 2006;Terzi, Keskin, & Taylan, 2006;Dahiya, Singh, Gaur, Garg, & Kushwaha, 2007;Icaga, 2007 2008; Hanbay, Turkoglu, & Demir, 2008;Dogan, Sengorur, & Koklu, 2009;Lermontov, Yokoyama, Lermontov, & Machado, 2009;Rehana & Mujumdar, 2009;Singh, Basant, Malik, & Jain, 2009;Ranković, Radulović, Radojević, Ostojić, & Čomić, 2010;Akkoyunlu, Altun, & Cigizoglu, 2011;Ay & Kisi, 2011Areerachakul, 2012;Hisar, Sönmez, Kaya, & Aras Hisar, 2012;Kisi & Ay, 2012;Qasaimeh et al, 2012;Sönmez, Hisar, & Yanık, 2012, 2013bSönmez, Hasiloglu, Hisar, Aras Mehan, & Kaya, 2013a;Chen & Liu, 2014;Emamgholizadeh, Kashi, Marofpoor, & Zalaghi, 2014;Heddam, 2014;Nemati, Naghipour, & Fazeli Fard, 2014;Yılmaz Öztürk, Akköz, Aşıkkutlu, & Gümüş, 2014;Ahmed & Shah, 2015;Alte & Sadgir, 2015;Csábrági, Molnár, Tanos, & Kovács, 2015, 2017...…”

Section: Introductionmentioning

confidence: 99%

Untitled

Sönmez¹,

Kale²,

Ozdemir³

et al. 2018

Turk. J. Fish. Aquat. Sci.

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Water quality is one of the main characteristics of a river system and prediction of water quality is the key factor in water resource management. Different physical, biological and chemical parameters including heavy metals can be used to assess river water quality. Evaluation of the water quality in the rivers is quite difficult and requires more time and effor t because of the fact that many factors affect water quality. Traditional data processing methods are insufficient to solve this problem. Therefore, in this study, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the concentrations of cadmium (Cd) in the Filyos River, Turkey. For this purpose, water samples collected at 7 sampling locations in the river during December 2014-2015 were used to develop ANFIS model. The available data set was apportioned into two separate sections for training and testing the ANFIS model. Developed models aimed to use the least parameters to estimate Cd concentration. As a result, a relatively higher correlation (R 2 =0.91) was found between observed and modelled Cd concentrations. The results indicated that the ANFIS model gave reasonable estimates for the concentrations of Cd with a high degree accuracy and robustness. In conclusion, this paper suggests that ANFIS methodology produce very successful findings and has the ability to predict Cd concentration in water resources. The outcomes of this research provide more information, simulation, and prediction about heavy metal concentration in natural aquatic ecosystems. Therefore, ANFIS can be used in further researches on water quality monitoring.

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A neural network-based approach for calculating dissolved oxygen profiles in reservoirs

Cited by 45 publications

References 12 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

Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models

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