Intelligent Earthquake Prediction System Based On Neural Network
Predicting earthquakes is an important issue in the
study of geography. Accurate prediction of earthquakes can help
people to take effective measures to minimize the loss of personal
and economic damage, such as large casualties, destruction of
buildings and broken of traffic, occurred within a few seconds.
United States Geological Survey (USGS) science organization
provides reliable scientific information about Earthquake Existed
throughout history & the Preliminary database from the National
Center Earthquake Information (NEIC) show some useful factors to
predict an earthquake in a seismic area like Aleutian Arc in the U.S.
state of Alaska. The main advantage of this prediction method that it
does not require any assumption, it makes prediction according to the
future evolution of the object's time series. The article compares
between simulation data result from trained BP and RBF neural
network versus actual output result from the system calculations.
Therefore, this article focuses on analysis of data relating to real
earthquakes. Evaluation results show better accuracy and higher
speed by using radial basis functions (RBF) neural network.
[1] Amr S. Elnashai, Luigi Di Sarno” Fundamentals of Earthquake
Engineering” A John Wiley & Sons, Ltd, Publication, 2008.
[2] S. G. Chern, R. F. Hu” FUZZ-ART neural networks for predicting chichi
earthquake induced liquefaction in yuan-lin area” Journal of Marine
Science and Technology, Vol. 10, No. 1, pp. 21-32, 2002.
[3] YueLiu, HuiLiu ” Extraction of If-Then Rules from Trained Neural
Network and Its Application to Earthquake Prediction” the Third IEEE
International Conference on Cognitive Informatics (ICCI’04) 2004.
[4] Yue Liu, Yuan Li” Constructive Ensemble of RBF Neural Networks and
Its Application to Earthquake Prediction” ISNN 2005, LNCS 3496, pp.
532.537, 2005.
[5] WANG Ying, CHEN Yi ” The Application of RBF Neural Network in
Earthquake Prediction” Third International Conference on Genetic and
Evolutionary Computing 2009.
[6] Hojjat Adeli, Ashif Panakkat” A probabilistic neural network for
earthquake magnitude prediction” H. Adeli, A. Panakkat / Neural
Networks 22, 1018_1024, 2009.
[7] Fangzhou Xu, Xianfeng Song” Neural Network Model for Earthquake
Prediction using DMETER Data and Seismic Belt Information” Second
WRI Global Congress on Intelligent Systems, 2010.
[8] CHEN Yi , ZHANG Jinkui” Research on Application of Earthquake
Prediction Based on Chaos Theory ” IEEE,2010.
[9] Guang-yu Geng, Chuang-hui Li” Research on Seismo-Ionospheric
Anomalies Using Artificial Neural Network” IEEE,2010.
[10] HUANG Sheng-Zhong” The prediction of the earthquake based on
neutral networks”, International Conference on Computer Design and
Applications (ICCDA), 2010.
[11] Habib Shah, Rozaida Ghazali, and Nazri Mohd Nawi ”Using Artificial
Bee Colony Algorithm for MLP Training on Earthquake Time Series
Data Prediction”, Journal of Computing, 2011.
[12] K. Tomiyasu ”lunar, solar and earthquake projected positions of 138
mag. 8.25-5.2 events in california from 1769 to 2004” IEEE,2012.
[13] J. Reyes, A. Morales-Esteban” Neural networks to predict earthquakes in
Chile” Reyes et al. / Applied Soft Computing 13, 1314–1328, 2013.
[14] Jui-Pin Wang1, Yun Xu” Earthquake statistics and a FOSM seismic
hazard analysis for a nuclear power plant in Taiwan”
[15] Zhuowei Hu, Lai Wei “Spatial Prediction of Earthquake-Induced
Secondary Landslide Disaster in Beichuan County Based on GIS”
Research Journal of Applied Sciences, Engineering and Technology
6(20): 3828-3837, 2013.
[16] S. Niksarlioglu, F. Kulahci “An Artificial Neural Network Model for
Earthquake Prediction and Relations between Environmental Parameters
and Earthquakes” World Academy of Science, Engineering and
Technology, 2013.
[17] Adel Moatti, Mohammad Reza Amin-Nasseri” Pattern Recognition on
Seismic Data for Earthquake Prediction Purpose” International
Conference on Environment, Energy, Ecosystems and Development,
2013
[18] A. Morales-Esteban, F. Martínez-Álvarez ”Earthquake prediction in
seismogenic areas of the Iberian Peninsula based on computational
intelligence” A. Morales-Esteban et al. / Tectonophysics 593, 121–134,
2013.
[19] Feiyan Zhou, Xiaofeng Zhu “Earthquake Prediction Based on LM-BP
Neural Network” Proceedings of the 9th International Symposium on
Linear Drives for Industry Applications, Volume 1, 2009.
[20] USGS National Earthquake Information Center,
http://earthquake.usgs.gov.
[21] David Nettleton” Commercial Data Mining Processing, Analysis and
Modeling for Predictive Analytics Projects” Elsevier Inc, 2014.
[22] Mark Hudson Beale,Martin T. Hagan” Neural Network Toolbox™
User’s Guide R2013b” The MathWorks, 2013.
[1] Amr S. Elnashai, Luigi Di Sarno” Fundamentals of Earthquake
Engineering” A John Wiley & Sons, Ltd, Publication, 2008.
[2] S. G. Chern, R. F. Hu” FUZZ-ART neural networks for predicting chichi
earthquake induced liquefaction in yuan-lin area” Journal of Marine
Science and Technology, Vol. 10, No. 1, pp. 21-32, 2002.
[3] YueLiu, HuiLiu ” Extraction of If-Then Rules from Trained Neural
Network and Its Application to Earthquake Prediction” the Third IEEE
International Conference on Cognitive Informatics (ICCI’04) 2004.
[4] Yue Liu, Yuan Li” Constructive Ensemble of RBF Neural Networks and
Its Application to Earthquake Prediction” ISNN 2005, LNCS 3496, pp.
532.537, 2005.
[5] WANG Ying, CHEN Yi ” The Application of RBF Neural Network in
Earthquake Prediction” Third International Conference on Genetic and
Evolutionary Computing 2009.
[6] Hojjat Adeli, Ashif Panakkat” A probabilistic neural network for
earthquake magnitude prediction” H. Adeli, A. Panakkat / Neural
Networks 22, 1018_1024, 2009.
[7] Fangzhou Xu, Xianfeng Song” Neural Network Model for Earthquake
Prediction using DMETER Data and Seismic Belt Information” Second
WRI Global Congress on Intelligent Systems, 2010.
[8] CHEN Yi , ZHANG Jinkui” Research on Application of Earthquake
Prediction Based on Chaos Theory ” IEEE,2010.
[9] Guang-yu Geng, Chuang-hui Li” Research on Seismo-Ionospheric
Anomalies Using Artificial Neural Network” IEEE,2010.
[10] HUANG Sheng-Zhong” The prediction of the earthquake based on
neutral networks”, International Conference on Computer Design and
Applications (ICCDA), 2010.
[11] Habib Shah, Rozaida Ghazali, and Nazri Mohd Nawi ”Using Artificial
Bee Colony Algorithm for MLP Training on Earthquake Time Series
Data Prediction”, Journal of Computing, 2011.
[12] K. Tomiyasu ”lunar, solar and earthquake projected positions of 138
mag. 8.25-5.2 events in california from 1769 to 2004” IEEE,2012.
[13] J. Reyes, A. Morales-Esteban” Neural networks to predict earthquakes in
Chile” Reyes et al. / Applied Soft Computing 13, 1314–1328, 2013.
[14] Jui-Pin Wang1, Yun Xu” Earthquake statistics and a FOSM seismic
hazard analysis for a nuclear power plant in Taiwan”
[15] Zhuowei Hu, Lai Wei “Spatial Prediction of Earthquake-Induced
Secondary Landslide Disaster in Beichuan County Based on GIS”
Research Journal of Applied Sciences, Engineering and Technology
6(20): 3828-3837, 2013.
[16] S. Niksarlioglu, F. Kulahci “An Artificial Neural Network Model for
Earthquake Prediction and Relations between Environmental Parameters
and Earthquakes” World Academy of Science, Engineering and
Technology, 2013.
[17] Adel Moatti, Mohammad Reza Amin-Nasseri” Pattern Recognition on
Seismic Data for Earthquake Prediction Purpose” International
Conference on Environment, Energy, Ecosystems and Development,
2013
[18] A. Morales-Esteban, F. Martínez-Álvarez ”Earthquake prediction in
seismogenic areas of the Iberian Peninsula based on computational
intelligence” A. Morales-Esteban et al. / Tectonophysics 593, 121–134,
2013.
[19] Feiyan Zhou, Xiaofeng Zhu “Earthquake Prediction Based on LM-BP
Neural Network” Proceedings of the 9th International Symposium on
Linear Drives for Industry Applications, Volume 1, 2009.
[20] USGS National Earthquake Information Center,
http://earthquake.usgs.gov.
[21] David Nettleton” Commercial Data Mining Processing, Analysis and
Modeling for Predictive Analytics Projects” Elsevier Inc, 2014.
[22] Mark Hudson Beale,Martin T. Hagan” Neural Network Toolbox™
User’s Guide R2013b” The MathWorks, 2013.
@article{"International Journal of Earth, Energy and Environmental Sciences:68603", author = "Emad Amar and Tawfik Khattab and Fatma Zada", title = "Intelligent Earthquake Prediction System Based On Neural Network", abstract = "Predicting earthquakes is an important issue in the
study of geography. Accurate prediction of earthquakes can help
people to take effective measures to minimize the loss of personal
and economic damage, such as large casualties, destruction of
buildings and broken of traffic, occurred within a few seconds.
United States Geological Survey (USGS) science organization
provides reliable scientific information about Earthquake Existed
throughout history & the Preliminary database from the National
Center Earthquake Information (NEIC) show some useful factors to
predict an earthquake in a seismic area like Aleutian Arc in the U.S.
state of Alaska. The main advantage of this prediction method that it
does not require any assumption, it makes prediction according to the
future evolution of the object's time series. The article compares
between simulation data result from trained BP and RBF neural
network versus actual output result from the system calculations.
Therefore, this article focuses on analysis of data relating to real
earthquakes. Evaluation results show better accuracy and higher
speed by using radial basis functions (RBF) neural network.
", keywords = "BP neural network, Prediction, RBF neural network.", volume = "8", number = "12", pages = "874-5", }
