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Cumhuriyet Science Journal
Article . 2018 . Peer-reviewed
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Cumhuriyet Science Journal
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An Adaptive Neuro-Fuzzy Inference System (ANFIS) of Radioactivity Levels in Hazar Lake

Authors: Miraç KAMIŞLIOĞLU; Fatih KÜLAHCI;

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

Abstract

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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