@article{(Open Science Index):https://publications.waset.org/pdf/10000062,
	  title     = {Intelligent Earthquake Prediction System Based On Neural Network},
	  author    = {Emad Amar and  Tawfik Khattab and  Fatma Zada},
	  country	= {},
	  institution	= {},
	  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.
},
	    journal   = {International Journal of Civil and Environmental Engineering},
	  volume    = {8},
	  number    = {12},
	  year      = {2014},
	  pages     = {874 - 878},
	  ee        = {https://publications.waset.org/pdf/10000062},
	  url   	= {https://publications.waset.org/vol/96},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 96, 2014},
	}