Disaster management is the systematic process and organization to prevent and mitigate the losses of women, environment, property.
As ultimate change is making the situation grieve, natural disaster knocks even wife of warning due to erratic put fears.
Now the loss can be more and widespread, in this area Big Data as the pout of 4th industrial revenue can be immensely beneficial.
What is Big Data?
- It is a term for data sets that are so large or complex that traditional data processing application software is adequate to deals.
- Can be understood as the Consolidation and centralization of public data from various spheres.
3Basics – Analyse – Data – Predict – Visualize.
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Significance in Disaster Management:
- Efficiently analyse the vast volume of data’s can help form format pattern of different Disaster events,
- Addresses storage issues – strong huge data’s can help to prevent mitigate, and prepare for disaster at different chains like from prediction i.e early warning system using Crips, Cries, remote sensing data’s to precision targeting.
- Geoinformatics infrastructure, transport network etc can help to locate people movement,
- Can collect huge communication data and connect people like Facebook Safety checking,
- Can fill the gap by knowing What, how much resources needed for mitigation.
- Can coordinate at the global level like other country giving earns warning of the cyclone, etc.
- Security and privacy evasion issues as huge volumes of personal data are stores to know our preferences, etc.
- Research and Development missing at academic and industrial level.
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- The government can involve academics, experts, intimation coordination in its development.
- India’s commitment to the 4th industrial revolution should be speeded up.
- ‘ Digital India ‘ is a great initiative to have more data’s. So proper infrastructure at the level of governance to implement it.
‘ Big Data ‘ model is a great development to deal with natural Disaster management at various levels, So all the challenges need to be tackled for its growth.