Notice of RetractionEffective feature selection for short-term earthquake prediction using Neuro-Fuzzy classifier

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Earthquakes is a serious sudden life threatening disaster for all kind of livings. Loss of life, property and depression are some common results of earthquakes for humankinds. The most important matter in this field is to predict earthquake time and strength. This study investigates an application of Neuro-Fuzzy classifier for short-term earthquake prediction using saved seismogram data. This method is able to predict earthquakes five minute before, with an acceptable accuracy (82.8571%). The features were obtained from statistical and entropy parameters, Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT), Chaotic Features (Maximum Lyapunov Exponent), estimated power spectral density (PSD), and the classifier used this extracted features to indicate whether the earthquake were takes place in the next following five minutes or not. Finally, after training of Neuro-Fuzzy classifier effective features were selected with UTA algorithm.