Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia

Ranković, Vesna; Radulović, Jasna; Radojević, Ivana D.; Ostojić, Аleksandar; Čomić, Ljiljana R.

doi:10.1016/j.ecolmodel.2009.12.023

Cited by 155 publications

(75 citation statements)

References 29 publications

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“…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). Furthermore, some intelligence algorithm such as genetic algorithm was used with ANN model for the management of the watershed water quality problem (Kuo et al 2006).…”

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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“…It relies on several factors such as oxygen depletion, salinity, temperature and others (Kalff, 2002;YSI, 2009). The level of dissolved oxygen is the criterion of health evaluation (Rankovic et al, 2010), that is repeatedly utilized for controlling of water quality at different aquatic systems such as rivers, reservoirs and wetlands (Singh et al, 2009;Ay and Kisi, 2012;Kisi et al, 2013). The modeling of water quality using either deterministic (Garcia et al, 2002;Hull et al, 2008;Shukla et al, 2008) or statistical approaches (Boano et al, 2006) have recently received great attention as a result of its significant role in environment and human health.…”

Section: Introductionmentioning

confidence: 99%

“…One of the paramount features of artificial neural network is their capability in handling large and complex systems with numerous interrelated parameters (Nourani et al, 2011). Artificial neural networks have successfully been utilized in a number of research concentrating on water quality prediction in lakes (Stefan et al, 1995), rivers (Singh et al, 2009;Niroobakhsh et al, 2012),reservoirs (Kuo et al, 2007;Rankovic et al, 2010;) and wastewater treatment (Abyaneh, 2014). Sarkar and Pandey (2015) modeled the water quality of River using Artificial Neural Network system.…”

Section: Introductionmentioning

confidence: 99%

PREDICTION OF DISSOLVED OXYGEN IN TIGRIS RIVER BY WATER TEMPERATURE AND BIOLOGICAL OXYGEN DEMAND USING ARTIFICIAL NEURAL NETWORKS (ANNs)

Omar

2017

juod

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The purpose of this study is to develop a feed-forward neural network (FFNN) model with back-propagation learning algorithm to predict the dissolved oxygen from water temperature and 5 days-biological oxygen demand in the Tigris River, Baghdad-Iraq. The Artificial Neural Networks model was implemented utilizing measured data that were gathered from laboratories of water treatment plant, Baghdad-Iraq, during the year 2008. The correlation analysis between dissolved oxygen and dependent parameters were utilized in selecting the major inputs from water quality parameters for commencing of ANN models. The performance of ANN models were tested utilizing the coefficient of correlation (R), the efficiency coefficient of Nash-Sutcliffe (NS), mean square error (MSE) and mean absolute errors (MAE). The outputs revealed that the feed-forward neural networks using back-propagation learning algorithm which was prepared by temperature and biological oxygen demand offered a relatively high correlation coefficient of 0.885, and efficiency coefficient of 0.782, meanwhile a reasonably low mean square errors of 1.133, and mean absolute errors of 0.369 values for whole array period. The results of the present study demonstrate that the artificial neural networks using FFNN model is capable to forecast the dissolved oxygen values with acceptable accuracy. This is suggesting that the artificial neural network is a useful tool for Tigris River management in Baghdad-Iraq.

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“…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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Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia

Cited by 155 publications

References 29 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 IN TIGRIS RIVER BY WATER TEMPERATURE AND BIOLOGICAL OXYGEN DEMAND USING ARTIFICIAL NEURAL NETWORKS (ANNs)

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