{"title":"Intelligent Earthquake Prediction System Based On Neural Network","authors":"Emad Amar, Tawfik Khattab, Fatma Zada","volume":96,"journal":"International Journal of Civil and Environmental Engineering","pagesStart":874,"pagesEnd":879,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10000062","abstract":"<p>Predicting earthquakes is an important issue in the<br \/>\r\nstudy of geography. Accurate prediction of earthquakes can help<br \/>\r\npeople to take effective measures to minimize the loss of personal<br \/>\r\nand economic damage, such as large casualties, destruction of<br \/>\r\nbuildings and broken of traffic, occurred within a few seconds.<br \/>\r\nUnited States Geological Survey (USGS) science organization<br \/>\r\nprovides reliable scientific information about Earthquake Existed<br \/>\r\nthroughout history &amp; the Preliminary database from the National<br \/>\r\nCenter Earthquake Information (NEIC) show some useful factors to<br \/>\r\npredict an earthquake in a seismic area like Aleutian Arc in the U.S.<br \/>\r\nstate of Alaska. The main advantage of this prediction method that it<br \/>\r\ndoes not require any assumption, it makes prediction according to the<br \/>\r\nfuture evolution of the object&#39;s time series. The article compares<br \/>\r\nbetween simulation data result from trained BP and RBF neural<br \/>\r\nnetwork versus actual output result from the system calculations.<br \/>\r\nTherefore, this article focuses on analysis of data relating to real<br \/>\r\nearthquakes. Evaluation results show better accuracy and higher<br \/>\r\nspeed by using radial basis functions (RBF) neural network.<\/p>\r\n","references":"[1] Amr S. Elnashai, Luigi Di Sarno\u201d Fundamentals of Earthquake\r\nEngineering\u201d A John Wiley & Sons, Ltd, Publication, 2008.\r\n[2] S. G. Chern, R. F. Hu\u201d FUZZ-ART neural networks for predicting chichi\r\nearthquake induced liquefaction in yuan-lin area\u201d Journal of Marine\r\nScience and Technology, Vol. 10, No. 1, pp. 21-32, 2002.\r\n[3] YueLiu, HuiLiu \u201d Extraction of If-Then Rules from Trained Neural\r\nNetwork and Its Application to Earthquake Prediction\u201d the Third IEEE\r\nInternational Conference on Cognitive Informatics (ICCI\u201904) 2004.\r\n[4] Yue Liu, Yuan Li\u201d Constructive Ensemble of RBF Neural Networks and\r\nIts Application to Earthquake Prediction\u201d ISNN 2005, LNCS 3496, pp.\r\n532.537, 2005.\r\n[5] WANG Ying, CHEN Yi \u201d The Application of RBF Neural Network in\r\nEarthquake Prediction\u201d Third International Conference on Genetic and\r\nEvolutionary Computing 2009.\r\n[6] Hojjat Adeli, Ashif Panakkat\u201d A probabilistic neural network for\r\nearthquake magnitude prediction\u201d H. Adeli, A. Panakkat \/ Neural\r\nNetworks 22, 1018_1024, 2009.\r\n[7] Fangzhou Xu, Xianfeng Song\u201d Neural Network Model for Earthquake\r\nPrediction using DMETER Data and Seismic Belt Information\u201d Second\r\nWRI Global Congress on Intelligent Systems, 2010.\r\n[8] CHEN Yi , ZHANG Jinkui\u201d Research on Application of Earthquake\r\nPrediction Based on Chaos Theory \u201d IEEE,2010.\r\n[9] Guang-yu Geng, Chuang-hui Li\u201d Research on Seismo-Ionospheric\r\nAnomalies Using Artificial Neural Network\u201d IEEE,2010.\r\n[10] HUANG Sheng-Zhong\u201d The prediction of the earthquake based on\r\nneutral networks\u201d, International Conference on Computer Design and\r\nApplications (ICCDA), 2010.\r\n[11] Habib Shah, Rozaida Ghazali, and Nazri Mohd Nawi \u201dUsing Artificial\r\nBee Colony Algorithm for MLP Training on Earthquake Time Series\r\nData Prediction\u201d, Journal of Computing, 2011.\r\n[12] K. Tomiyasu \u201dlunar, solar and earthquake projected positions of 138\r\nmag. 8.25-5.2 events in california from 1769 to 2004\u201d IEEE,2012.\r\n[13] J. Reyes, A. Morales-Esteban\u201d Neural networks to predict earthquakes in\r\nChile\u201d Reyes et al. \/ Applied Soft Computing 13, 1314\u20131328, 2013.\r\n[14] Jui-Pin Wang1, Yun Xu\u201d Earthquake statistics and a FOSM seismic\r\nhazard analysis for a nuclear power plant in Taiwan\u201d\r\n[15] Zhuowei Hu, Lai Wei \u201cSpatial Prediction of Earthquake-Induced\r\nSecondary Landslide Disaster in Beichuan County Based on GIS\u201d\r\nResearch Journal of Applied Sciences, Engineering and Technology\r\n6(20): 3828-3837, 2013.\r\n[16] S. Niksarlioglu, F. Kulahci \u201cAn Artificial Neural Network Model for\r\nEarthquake Prediction and Relations between Environmental Parameters\r\nand Earthquakes\u201d World Academy of Science, Engineering and\r\nTechnology, 2013.\r\n[17] Adel Moatti, Mohammad Reza Amin-Nasseri\u201d Pattern Recognition on\r\nSeismic Data for Earthquake Prediction Purpose\u201d International\r\nConference on Environment, Energy, Ecosystems and Development,\r\n2013\r\n[18] A. Morales-Esteban, F. Mart\u00ednez-\u00c1lvarez \u201dEarthquake prediction in\r\nseismogenic areas of the Iberian Peninsula based on computational\r\nintelligence\u201d A. Morales-Esteban et al. \/ Tectonophysics 593, 121\u2013134,\r\n2013.\r\n[19] Feiyan Zhou, Xiaofeng Zhu \u201cEarthquake Prediction Based on LM-BP\r\nNeural Network\u201d Proceedings of the 9th International Symposium on\r\nLinear Drives for Industry Applications, Volume 1, 2009.\r\n[20] USGS National Earthquake Information Center,\r\nhttp:\/\/earthquake.usgs.gov.\r\n[21] David Nettleton\u201d Commercial Data Mining Processing, Analysis and\r\nModeling for Predictive Analytics Projects\u201d Elsevier Inc, 2014.\r\n[22] Mark Hudson Beale,Martin T. Hagan\u201d Neural Network Toolbox\u2122\r\nUser\u2019s Guide R2013b\u201d The MathWorks, 2013.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 96, 2014"}