Fuzzy logic modeling of the dissolved oxygen fluctuations in Golden Horn

Altunkaynak, Abdüsselam; Özger, Mehmet; Çakmakçı, Mehmet

doi:10.1016/j.ecolmodel.2005.03.007

Cited by 65 publications

(30 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…according to the corresponding degrees and numbers of specific membership functions used in the fuzzy inference system (FIS) (Altunkaynak et al, 2005;Yetilmezsoy, 2012). The input is always a crisp numerical value of the input variable (i.e., in the present case, for wind speed (WS), the interval between 0.82 and 3.73 m/s) and the output is a fuzzified degree of membership in the qualifying linguistic set (always the interval between 0 and 1).…”

Section: Accuracy or Detection Limit Air Pollutantsmentioning

confidence: 99%

A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

Yetilmezsoy

Abdul‐Wahab

2012

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

A prognostic approach is proposed based on a fuzzy-logic model to estimate suspended dust concentrations, related to PM 10 , in a specific residential area in Kuwait with high traffic and industrial influences. Seven input variables, including four important meteorological parameters (wind speed, wind direction, relative humidity and solar radiation) and the ambient concentrations of three gaseous pollutants (methane, carbon monoxide and ozone) were fuzzified using a sytem with a graphical user interface (GUI) and an artificial intelligence-based approach. Trapezoidal membership functions with ten and fifteen levels were employed for the fuzzy subsets of each model variable. A Mamdani-type fuzzy inference system (FIS) was developed to introduce a total of 146 rules in the IF-THEN format. The product (prod) and the centre of gravity (centroid) methods were performed as the inference operator and defuzzification methods, respectively, for the proposed FIS. The results obtained using uzzy-logic were compared with the outputs of an exponential regression model. The predictive performances of the models were compared based on various descriptive statistical indicators, and the proposed method was tested against additional observed data. The prognostic model presented in this work produced very small deviations from the actual results, and showed better predictive performance than the other model with regard to forecasting PM 10 levels, with a very high determination coefficient of over 0.99.

show abstract

Section: Accuracy or Detection Limit Air Pollutantsmentioning

confidence: 99%

A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

Yetilmezsoy

Abdul‐Wahab

2012

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

“…ÜNSAL, 1988;TUNCER et al, 2001;ASLAN-YILMAZ et al, 2004;ALTUNKAYNAK et al, 2005). The most comprehensive one, with a holistic approach (YÜKSEK et al, 2006), is assessing environmental changes through macro-and microbiological data and water quality parameters.…”

Section: Introductionmentioning

confidence: 99%

Golden Horn Estuary: Description of the ecosystem and an attempt to assess its ecological quality status using various classification metrics

Albayrak

Balkıs

et al. 2010

Medit. Mar. Sci.

View full text Add to dashboard Cite

In this paper, we describe the pelagic and benthic ecosystem of the Golden Horn estuary opening into the Marmara Sea. To improve the water quality of the estuary, which had long been subject to severe anthropogenic pollution (industrial, chemical, shipping), industrial facilities were moved from the estuary in the 1980s, followed by a rehabilitation plan in the 1990s. Our results, based on chemical parameters and phytoplankton showed some signs of improvement of water conditions in the upper layer. However, macrozoobenthic findings of this study did not reflect such a recovery in bottom life.An approach to the Ecological Quality Status (EQS) assessment was performed by applying the biotic indices BENTIX, AMBI, BOPA, BO2A. Our final assessment was based on 'expert-judgements' and revealed a very disturbed overall ecosystem with 'bad' EQS for the station at the head of the estuary, 'poor' in the rest of the estuary and 'moderate' EQS only in the middle station.

show abstract

Fuzzy logic modeling of the dissolved oxygen fluctuations in Golden Horn

Cited by 65 publications

References 25 publications

A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

Untitled

Golden Horn Estuary: Description of the ecosystem and an attempt to assess its ecological quality status using various classification metrics

Contact Info

Product

Resources

About