Adaptive Neuro-Fuzzy Logic System for Heavy Metal Sorption in Aquatic Environments

Qasaimeh, Ahmad; Abdallah, Mohammad; Hani, Falah Bani

doi:10.4236/jwarp.2012.45030

Cited by 14 publications

(10 citation statements)

References 24 publications

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“…Also, Rebouh et al [35] applied the ANFIS technique for the prediction of metal ions removal capacity from aqueous solution by wheat straw biosorbent. Moreover, Qasaimeh et al exploited the ANFIS to assess the conditions required for aquatic systems to serve as a sink for metal ions removal [36]. The adsorption modeling basically has two advantages: (1) development a relation between input and output effective parameters irrespective of behavior and (2) finding the optimal condition for the removal of target pollutant which has been verified by confirmatory experimental run.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

2016

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In the present paper, the adaptive neural fuzzy inference system (ANFIS) was used for modeling of magnetic chitosan adsorption performance for the methyl orange removal. The ANFIS network, which is the best in data predicting, was trained with back propagation optimum method. Our results revealed that the developed ANFIS models can effectively model the non-linearity behavior of the adsorptive performance and the predicted values were in good agreement with the experimental data.

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Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

2016

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“…Yılmaz Öztürk et al (2014) applied fuzzy logic assessment method for heavy metal pollution in Apa Dam Lake and indicated that fuzzy logic methods suggested more precise evaluation than traditional classification methods. Qasaimeh et al (2012) used ANFIS for heavy metal sorption in aquatic environments and found a higher correlation of 0.98 between observed data and modelled results. Yesilnacar and Sahinkaya (2012) used ANN for prediction of sulfate and sodium adsorption ratio in an unconfined aquifer in Turkey.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, releasing of Cd into the aquatic environment from industrial activities is a serious threat for the aquatic ecosystems (Qasaimeh, Abdallah, & Bani Hani, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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“…Genetic algorithms (GA) can be applied to natural and real world problems. For instance, a fuzzy logic system used the genetic algorithms and neural networks to create fuzzy knowledge bases and rules to evaluate the capability of wetlands to remove mercury [17] [18].…”

Section: Natural Selectionmentioning

confidence: 99%

Genetic Algorithm Optimization for Multi-Biogas Mass Transfer in Hydrophobic Polymer Biocell

Qasaimeh¹,

Masoud²,

Sharie³

2015

JSBS

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In this work, a new municipal biocell with new operation and waste management is proposed. The proposed system is biocell that is built gradually when the waste is being disposed. Different from conventional disposal at landfills, waste is put between "hydrophobic bricks" that are perforated permeable containments filled up with porous dumping material such as styrofoam. Genetic algorithm is used to optimize a transfer function that represents input of biogas percentages and output solutions for daily mass transfer rates for biogas mixture from which mass and volume of biogas within the biocell time of service are calculated. Transfer function is obtained by fitting dynamic input-output data to the input-output solutions. Input-output data are encoded to chromosomes (1, 0 digits). These chromosomes are subjected to genetic processes as crossover and mutations. Then a process of evaluation takes place. The evaluation process entails an objective function that evaluates the squared difference between experimental and calculated values. After the chromosomes are being evaluated, they are either selected for more iteration or decoded to the solutions. The decoding process is performed on optimal chromosomes to obtain optimal solutions and required optimal transfer function. Consequently, the mass and volume of biogas within the landfill time of service are determined for any ratio of CH4:CO2 in biocell.

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Adaptive Neuro-Fuzzy Logic System for Heavy Metal Sorption in Aquatic Environments

Cited by 14 publications

References 24 publications

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

Untitled

Genetic Algorithm Optimization for Multi-Biogas Mass Transfer in Hydrophobic Polymer Biocell

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