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A formulation of big data analytics model in strengthening the disaster risk reduction

Zayid, S. and Bakar, N. A. A. and Valachamy, M. and Malek, N. S. A. and Yaacob, S. and Hassan, N. H. (2020) A formulation of big data analytics model in strengthening the disaster risk reduction. Journal of Environmental Treatment Techniques, 8 (1). pp. 481-487. ISSN 2309-1185

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A natural disaster is a serious event that contributes to the damage of infrastructures and property losses, the demand of budgetary allocation, disruption of economic and social activities, damages to the environment, and threat to human life. In disaster management, one of the aims is to reduce the impact of natural disaster through disaster risk management. However, the traditional data risk management mechanism to store and analyse huge disasters has become a challenge for relevant organizations due to its massive datasets, especially when it deals with big data and analytics. Therefore, the aim of this paper is to formulate a big data analytics model to strengthen the disaster risk reduction for Selangor State, Malaysia, comprehending both traditional datasets (geospatial data) and big data analytics (nonspatial data). To this end, 59 factors and available datasets were classified into six categories: ecology, economic, environment, organisation, social, and technology. These factors were derived from existing studies and then validated in a focus group discussion with 54 government agencies involved disaster risk management in Selangor State, Malaysia. The final output of this paper is Big Data Analytics Model for Disaster Risk Reduction, which will be useful to all stakeholders related to disaster risk management and disaster risk reduction initiatives.

Item Type:Article
Uncontrolled Keywords:disaster risk management, disaster risk reduction, Selangor state
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:86448
Deposited By: Narimah Nawil
Deposited On:08 Sep 2020 21:18
Last Modified:09 Sep 2020 15:21

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