A survey on Predictive data mining techniques for disaster prediction
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Arjun Singh
|DOI : 10.14445/22315381/IJETT-V30P242|
Arjun Singh"A survey on Predictive data mining techniques for disaster prediction", International Journal of Engineering Trends and Technology (IJETT), V30(5),223-227 December 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
The world is unpredictable and random in nature.Some of events are human generated and some of them are nature inspired. Among these events the natural events are change the face of leaving and also impact on the human life. In such events the disasters are most of the time affecting human and their daily life. In this paper a survey on the disasters and their effects are prepared first. Then after a technique is introduced to perform the text and news analysis by which the location of disaster can be predictable. The proposed data model is used to analyse the text and HTML documents for making the prediction. That technique is not used to predict the disaster before it occurred, it helps to discover the locations of disasters for improving recovery operations.
 R. W. Perry, E. L. Quarantelli, “WHAT IS A DISASTER”,
Copyright © 2005 by International Research Committee on
 M. E. Baird, “The “Phases” of Emergency Management”, Vanderbilt Center for Transportation Research (VECTOR), January 2010
 An improved K-Means clustering algorithm, Juntao Wang, Xiaolong Su, 978-1-61284-486-2/111$26.00 ©2011 IEEE
 ShwetaJaiswal, Atish Mishra, Praveen Bhanodia, “Grid Host Load Prediction Using GridSim Simulation and Hidden Markov Model”, International Journal of Emerging Technology and Advanced EngineeringISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 7, July 2014
 RoshaniChoudhary, JagdishRaikwal, “An Ensemble Approach to Enhance Performanceof Webpage Classification”, International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5614-5619
Disaster management, data mining, prediction, text and news analysis, recovery.