title: Artificial neural network model for earthquake prediction with radon monitoring.
abstract: 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.
authors.affiliation: Science & Art Faculty, Physics Department, Firat University, Elaziğ 23169, Turkey.
authors.forename: Fatih
authors.initials: F
authors.lastname: Külahci
authors.forename: Murat
authors.initials: M
authors.lastname: Inceöz
authors.forename: Mahmut
authors.initials: M
authors.lastname: Doğru
authors.forename: Ercan
authors.initials: E
authors.lastname: Aksoy
authors.forename: Oktay
authors.initials: O
authors.lastname: Baykara
mesh_terms: D000465:Algorithms; D055866:Earthquakes; D016571:Neural Networks, Computer; D011886:Radon / Q000032:analysis*; D013995:Time
chemical_list: D011886:Radon