Application of neural network and ANFIS model for

Authors: Ahmad Zamani Mohammad Reza Sorbi Ali Akbar Safavi

Publish Date: 2013/04/04

Volume: 6, Issue: 2, Pages: 71-85

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Abstract

This study examined the spatialtemporal variations in seismicity parameters for the September 10th 2008 Qeshm earthquake in south Iran To this aim artificial neural networks and Adaptive Neural Fuzzy Inference System ANFIS were applied The supervised Radial Basis Function RBF network and ANFIS model were implemented because they have shown the efficiency in classification and prediction problems The eight seismicity parameters were calculated to analyze spatial and temporal seismicity pattern The data preprocessing that included normalization and Principal Component Analysis PCA techniques was led before the data was fed into the RBF network and ANFIS model Although the accuracy of RBF network and ANFIS model could be evaluated rather similar the RBF exhibited a higher performance than the ANFIS for prediction of the epicenter area and time of occurrence of the 2008 Qeshm main shock A proper training on the basis of RBF network and ANFIS model might adopt the physical understanding between seismic data and generate more effective results than conventional prediction approaches The results of the present study indicated that the RBF neural networks and the ANFIS models could be suitable tools for accurate prediction of epicenteral area as well as time of occurrence of forthcoming strong earthquakes in active seismogenic areasThis research was supported by the Center of Excellence for Environmental Geohazards and the Research Council of Shiraz University The authors express their gratitude to Stefan Wiemer for the ZMAP software MRS is grateful to B Rahnama D Eberhard Gh Nasuhi and A Khosravani for valuable comments MRS sincerely thanks Z Heidari for editing the manuscript The autors highly appreciate the Referees for their interest in our work and for insightful comments that will greatly improve the manuscript

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