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Combating disaster prone zone by prioritizing attributes with hybrid clustering and ANP approach
Srivastava Rashi,Sabitha Sai,Majumdar Rana,Choudhury Tanupriya,Dewangan Bhupesh Kumar 대한공간정보학회 2021 Spatial Information Research Vol.29 No.4
Due to a lack of assets and high improbability, developing an information system for every disaster situation is a challenging task. Consequently, building an information system for such a situation is essential, and the latest research direction has emphasized on development of such a system which varies due to the irregular nature of environments. This work emphasizes the data mining technique based on available disaster data or the earlier prediction of such occurrences to combat damages. The data mining technique is applied to the clustering of data for smooth processes of the obtained data. K-means clustering and analytic network process (ANP) are implemented as unsupervised learning for initial data and to find groups in the data, clustered based on feature similarity. The proposed approach implies an effective tool for predicting impacts in terms of hazards and this paper also evaluates its effectiveness. This study offers important insights into the disaster recovery practitioner to select the best disaster recovery solution and prioritize them for their enterprise.
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