Artificial neural network model for earthquake prediction with radon monitoring.
Apart from the linear monitoring studies concerning the relationship between radon and earthquake, an artificial neural networks (ANNs) model approach is presented starting out from non-linear changes of the eight different parameters during the earthquake occurrence. A three-layer Levenberg-Marquardt feedforward learning algorithm is used to model the earthquake prediction process in the East Anatolian Fault System (EAFS). The proposed ANN system employs individual training strategy with fixed-weight and supervised models leading to estimations. The average relative error between the magnitudes of the earthquakes acquired by ANN and measured data is about 2.3%. The relative error between the test and earthquake data varies between 0% and 12%. In addition, the factor analysis was applied on all data and the model output values to see the statistical variation. The total variance of 80.18% was explained with four factors by this analysis. Consequently, it can be concluded that ANN approach is a potential alternative to other models with complex mathematical operations.
Külahci
Fatih
F
Science & Art Faculty, Physics Department, Firat University, Elaziğ 23169, Turkey.
Inceöz
Murat
M
Doğru
Mahmut
M
Aksoy
Ercan
E
Baykara
Oktay
O
D000465:Algorithms; D055866:Earthquakes; D016571:Neural Networks, Computer; D011886:Radon / Q000032:analysis*; D013995:Time
D011886:Radon