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.
Keywords
References
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Details
Primary Language
English
Subjects
-
Journal Section
Conference Paper
Publication Date
June 29, 2018
Submission Date
December 2, 2017
Acceptance Date
March 1, 2018
Published in Issue
Year 2018 Volume: 39 Number: 2