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Application of Neural Network Based on Genetic Algorithm in Predicting Magnitude of Earthquake in North Tabriz Fault (NW Iran)


Affiliations
1 University of Tabriz, Tabriz, Iran, Islamic Republic of
 

Here we present an application of a supervised feed forward artificial neural network (ANN) that is trained on the basis of genetic algorithm (GA). The network model is used for predicting the magnitude of earthquakes in the North Tabriz Fault (NTF) North-west Iran. The earthquake database was derived from the catalogues of both the International Institute of Earthquake Engineering and Seismicity of Iran and the Iranian Seismological Center. For this purpose, three temporal seismicity parameters were calculated using the ZMAP MATLAB toolbox. The performance of the artificial neural network (ANN) model was measured in terms of accuracy by a ten-fold cross-validation as 99.11%. Another evaluation method was predicting a case event that occurred on 11 August 2012 in Ahar-Varzeghan in Iran. Results showed that the ANN optimized with GA (ANNGA) learning optimization model is suitable and may be useful for predicting future earthquakes, especially in active seismologic regions.

Keywords

Artificial Neural Network, Earthquake Magnitude, Genetic Algorithm, Temporal Seismicity Features.
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  • Application of Neural Network Based on Genetic Algorithm in Predicting Magnitude of Earthquake in North Tabriz Fault (NW Iran)

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Authors

Negar Sadat Soleimani Zakeri
University of Tabriz, Tabriz, Iran, Islamic Republic of
Saeid Pashazadeh
University of Tabriz, Tabriz, Iran, Islamic Republic of

Abstract


Here we present an application of a supervised feed forward artificial neural network (ANN) that is trained on the basis of genetic algorithm (GA). The network model is used for predicting the magnitude of earthquakes in the North Tabriz Fault (NTF) North-west Iran. The earthquake database was derived from the catalogues of both the International Institute of Earthquake Engineering and Seismicity of Iran and the Iranian Seismological Center. For this purpose, three temporal seismicity parameters were calculated using the ZMAP MATLAB toolbox. The performance of the artificial neural network (ANN) model was measured in terms of accuracy by a ten-fold cross-validation as 99.11%. Another evaluation method was predicting a case event that occurred on 11 August 2012 in Ahar-Varzeghan in Iran. Results showed that the ANN optimized with GA (ANNGA) learning optimization model is suitable and may be useful for predicting future earthquakes, especially in active seismologic regions.

Keywords


Artificial Neural Network, Earthquake Magnitude, Genetic Algorithm, Temporal Seismicity Features.

References





DOI: https://doi.org/10.18520/cs%2Fv109%2Fi9%2F1722-1729