LEVERAGING AWS ANALYTICS FOR OPTIMIZED NATURAL DISASTER RESPONSE AND EFFECTIVE RESOURCE ALLOCATION

Abstract

An extensive suite of services that can be utilized for improving Natural Disaster (ND) management across its four phases, namely mitigation, preparedness, response, and recovery, is provided by Amazon Web Services (AWS). Nevertheless, there is an inadequate interpretation of the role of AWS in Disaster Response (DR) Resource Allocation (RA), particularly for NDs. Hence, investigating the impact of AWS analytics in optimizing RA for effective natural DR is the aim of the present study. In the study, data are gathered from various secondary sources. To analyze the secondary data, the study employs a qualitative approach. The RA for natural DR includes Amazon Location service, Amazon S3, Amazon Redshift, Amazon QuickSight, and AWS Lambda and Amazon SNS for assessing geospatial information, storing data, identifying optimal allocation, visualization, and sending notifications, respectively. Additionally, as per the study, AWS enhances the efficacy of allocating emergency resources in response to NDs. Furthermore, AWS permits rapid deployment of disaster recovery solutions, decreasing downtime than conventional approaches. According to the overall result of the study, the utilization of AWS analytics can forecast resource demands and optimize RA for natural DR and recovery. Additionally, utilizing AWS analytics for natural DR makes a contribution to saving lives, diminishing damage, and augmenting the efficiency of relief efforts by rendering data-driven insights. Recovery organizations can move from a reactive approach to a proactive and optimized one through the collection and investigation of huge amounts of data.