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Hazar Gölü’ndeki Radyoaktif Seviyelerin Belirlenmesi için Uyarlamalı Sinirsel-Bulanık Çıkarım Sistemi (ANFIS) ile Modellenmesi

Yıl 2018, Cilt: 39 Sayı: 2, 413 - 423, 29.06.2018

Miraç Kamışlıoğlu , Fatih Külahcı

https://doi.org/10.17776/csj.360319

Öz

Bu çalışmada, bir Adaptif Sinirsel Bulanık
Çıkarım Sistemi (ANFIS) modeli Hazar Gölü (Türkiye) sularının alfa
radyoaktivitesinin belirlenmesi ve onun bilinmeyen değerlerinin öngörülmesi
için önerilmiştir. Model parametreleri olarak pH, toplam sertlik (TH), derinlik,
elektriksel iletkenlik (EC) ve göl suyunun toplam alfa radyoaktivitesi
belirlenmiştir. ANFIS modeli beş tabakalı yapı için geri-yayılım algoritması
kullanılarak gerçekleştirilmiştir. Teorik ve deneysel (ANFIS) tahmin sonuçları
arasındaki ortalama rölatif hata 0.7043% dir. Test verileriyle radyoaktivite
verileri arasındaki rölatif hata 0.06% ve 14% arasında değişmiştir. Ek olarak,
ANFIS modelinin geçerliliği regresyon modeli ile de test edilmiştir. ANFIS
modeli istatistiksel olarak regresyon modelinden daha güvenilir sonuçlar
vermiştir.

Anahtar Kelimeler

ANFIS Radyoaktivite Tahmini Bulanık Çıkarım Sistemi Hazar Gölü

Kaynakça

  • [1]. Dragovic S. and Antonije O., Classification of soil samples according to geographic origin using gamma-ray spectrometry and pattern recognition methods. Appl Radiat. Isotopes., 65 (2007) 218-224.
  • [2]. Baylar A. Hanbay D. and Batan M., Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs, Expert Syst. Appl., 36 (2009) 8368-8374.
  • [3]. Külahcı F. İnceöz M. Doğru M. Aksoy E. and Baykara O., Artificial neural network model for earthquake prediction with radon monitoring. Appl Radiat. Isotopes., 67 (2009) 212–219.
  • [4]. Gueldal V. and Tongal H., Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir Lake level forecasting. Water Resour Manag, 24 (2010) 105-128.
  • [5]. Talebizadeh M. and Moridnejad A., Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models, Expert Syst. Appl., 38 (2011) 4126-4135.
  • [6]. Abed-Erndoust A. and Kerachian R., Wave height prediction using the rough set theory. Ocean Eng., 54 (2012) 244-250.
  • [7]. Kisi O. Shiri J. and Nikoofar B., Forecasting daily lake levels using artificial intelligence approaches, Comput Geosci., 41 (2012) 169-180.
  • [8]. Kulahci F. Özer AB. and Doğru M., Prediction of the radioactivity in Hazar Lake (Sivrice, Turkey) by artificial neural networks, J Radioanal Nucl Ch., 269 (2006) 63-68.
  • [9]. Kulahci F. and Dogru M., The physical and chemical researches in water and sediment of Keban Dam Lake, Turkiye: part 1- radioactivity iso-curves., J Radioanal Nucl Ch., 268 (2006) 517-528.
  • [10]. Kulahci F. and Doğru M., Physical and chemical investigation of water and sediment of the Keban Dam Lake, Turkey: Part 2: Distribution of radioactivity, heavy metals and major elements, J. Radioanal. Nucl. Ch., 268 (2006b) 529–537.
  • [11]. Marsequera M., Model identification by neuro-fuzzy techniques: predicting the water level in a steam generator of a PWR, Prog Nucl Energ., 44 (2004) 237-252.
  • [12]. Tokalioglu S. and Kartal S., Chemometrical interpretation of lake waters after their chemical analysis by using AAS flame photometry and titrimetric techniques, Int J Environ An Ch., 82 (2002) 291-305.
  • [13]. Krieger LH., Interim radiochemical methodology from drinking water., EPA 600(4), 75-008, Cincinnati, Ohio,1975 pp 20-100.
  • [14]. Jang JSR., ANFIS: Adaptive-network-based fuzzy inference system, IEEE T Syst Man Cy., 23 (1993) 665-685.
  • [15]. Takagi T., and Sugeno M., Fuzzy identification of systems and its applications to modeling and control. IEEE T Syst Man Cy., 15 (1985) 116-132.
  • [16]. Takagi H, and Hayashi I., NN-driven fuzzy reasoning., Int J Approx Reason., 5 (1991) 91-212.
  • [17]. Melin P. and Castillo O., Intelligent control of a steepping motor drive using an adaptive neuro- fuzzy inference system, Inform Sciences., 170 (2005) 133-151.
  • [18]. Mon YJ., Airbag controller designed by adaptive- network-based fuzzy inference system (ANFIS), Fuzzy Sets Syst., 158 (2007) 2706-2714.
  • [19]. Kamışlıoğlu M. and Külahcı F., Chaotic Behavior of Soil Radon Gas and Applications, Acta Geophysica., 64(5) (2016) 1563-1592.

An Adaptive Neuro-Fuzzy Inference System (ANFIS) of Radioactivity Levels in Hazar Lake

Yıl 2018, Cilt: 39 Sayı: 2, 413 - 423, 29.06.2018

Miraç Kamışlıoğlu , Fatih Külahcı

https://doi.org/10.17776/csj.360319

Öz

In
this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model is proposed
for the determination of alpha radioactivity of Hazar Lake waters and for the
prediction of its unknown values. The model parameters of the lake water are
pH, total hardness (TH), depth, electrical conductivity (EC), and alpha
radioactivity. ANFIS model is performed using the back-propagation algorithm,
which has the five layers. Average relative error between measurements
predicted by theoretical (ANFIS) and experimental data is approximately
0.7043%. The relative error between the test data and the radioactivity data
change between 0.06% and 14%. Additionally, validity of the model is also
tested with a regression model. The predicted results with the ANFIS model is
better as statistically than the regression model.

Anahtar Kelimeler

ANFIS Prediction of Radioactivity Fuzzy Inference System Hazar Lake

Kaynakça

  • [1]. Dragovic S. and Antonije O., Classification of soil samples according to geographic origin using gamma-ray spectrometry and pattern recognition methods. Appl Radiat. Isotopes., 65 (2007) 218-224.
  • [2]. Baylar A. Hanbay D. and Batan M., Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs, Expert Syst. Appl., 36 (2009) 8368-8374.
  • [3]. Külahcı F. İnceöz M. Doğru M. Aksoy E. and Baykara O., Artificial neural network model for earthquake prediction with radon monitoring. Appl Radiat. Isotopes., 67 (2009) 212–219.
  • [4]. Gueldal V. and Tongal H., Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir Lake level forecasting. Water Resour Manag, 24 (2010) 105-128.
  • [5]. Talebizadeh M. and Moridnejad A., Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models, Expert Syst. Appl., 38 (2011) 4126-4135.
  • [6]. Abed-Erndoust A. and Kerachian R., Wave height prediction using the rough set theory. Ocean Eng., 54 (2012) 244-250.
  • [7]. Kisi O. Shiri J. and Nikoofar B., Forecasting daily lake levels using artificial intelligence approaches, Comput Geosci., 41 (2012) 169-180.
  • [8]. Kulahci F. Özer AB. and Doğru M., Prediction of the radioactivity in Hazar Lake (Sivrice, Turkey) by artificial neural networks, J Radioanal Nucl Ch., 269 (2006) 63-68.
  • [9]. Kulahci F. and Dogru M., The physical and chemical researches in water and sediment of Keban Dam Lake, Turkiye: part 1- radioactivity iso-curves., J Radioanal Nucl Ch., 268 (2006) 517-528.
  • [10]. Kulahci F. and Doğru M., Physical and chemical investigation of water and sediment of the Keban Dam Lake, Turkey: Part 2: Distribution of radioactivity, heavy metals and major elements, J. Radioanal. Nucl. Ch., 268 (2006b) 529–537.
  • [11]. Marsequera M., Model identification by neuro-fuzzy techniques: predicting the water level in a steam generator of a PWR, Prog Nucl Energ., 44 (2004) 237-252.
  • [12]. Tokalioglu S. and Kartal S., Chemometrical interpretation of lake waters after their chemical analysis by using AAS flame photometry and titrimetric techniques, Int J Environ An Ch., 82 (2002) 291-305.
  • [13]. Krieger LH., Interim radiochemical methodology from drinking water., EPA 600(4), 75-008, Cincinnati, Ohio,1975 pp 20-100.
  • [14]. Jang JSR., ANFIS: Adaptive-network-based fuzzy inference system, IEEE T Syst Man Cy., 23 (1993) 665-685.
  • [15]. Takagi T., and Sugeno M., Fuzzy identification of systems and its applications to modeling and control. IEEE T Syst Man Cy., 15 (1985) 116-132.
  • [16]. Takagi H, and Hayashi I., NN-driven fuzzy reasoning., Int J Approx Reason., 5 (1991) 91-212.
  • [17]. Melin P. and Castillo O., Intelligent control of a steepping motor drive using an adaptive neuro- fuzzy inference system, Inform Sciences., 170 (2005) 133-151.
  • [18]. Mon YJ., Airbag controller designed by adaptive- network-based fuzzy inference system (ANFIS), Fuzzy Sets Syst., 158 (2007) 2706-2714.
  • [19]. Kamışlıoğlu M. and Külahcı F., Chaotic Behavior of Soil Radon Gas and Applications, Acta Geophysica., 64(5) (2016) 1563-1592.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Natural Sciences
Yazarlar

Miraç Kamışlıoğlu

Fatih Külahcı

Yayımlanma Tarihi 29 Haziran 2018
Gönderilme Tarihi 2 Aralık 2017
Kabul Tarihi 1 Mart 2018
Yayımlandığı Sayı Yıl 2018Cilt: 39 Sayı: 2

Kaynak Göster

APA Kamışlıoğlu, M., & Külahcı, F. (2018). An Adaptive Neuro-Fuzzy Inference System (ANFIS) of Radioactivity Levels in Hazar Lake. Cumhuriyet Science Journal, 39(2), 413-423. https://doi.org/10.17776/csj.360319