Disaster management is a systematic process with primary aim to reduce the negative consequences and effect of disasters, hence safeguarding people and social infrastructure. Effective management as well as monitoring of disasters is a global challenge. As the number and access to different datasets is expanding rapidly, the potential and utility of big data is growing for disaster management.

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Importance of Big Data in disaster management:

• Empower decision-makers to make accurate assessment during a disaster.
• Big data generated from geo-informatics and remote sensing platforms can contribute to early warning systems for disasters.
• Integration of different data streams, along with data processing and storage is effective for disaster preparedness.
• Help in the development of effective strategies and contribute to minimize the potential effects of disasters.
• Significance of big data analytics to predict occurrences of the floods and for flood management.
• It will help for timely humanitarian response to different disasters.
• Using geospatial datasets along with big data paradigm can provide location based services to avoid hazardous situations.
• It will also benefit in the identification of regions which need the most urgent attention.
• Enhance disaster recovery by utilizing community information and connecting victims with emergency responders and family.
• Connecting Missing People with Their Families. E.g.: Facebook mark safe.
• With big data, safety professionals can better prepare disaster simulations for more accurate implementations.
• Big Data opens up new career opportunities for those who want to find innovative ways to help others

Limitations:

• Understanding how to link different datasets with different kinds of disaster
• The potential of big data technology has not been fully explored for disaster management.
• Not all big data is public and freely available.
• Network security threats and vulnerabilities.
• Challenges related to protection of personal information and privac

Way Forward:

• Integration of datasets along with providing access to information to agencies managing disasters is crucial to enable effective decision making.
• It’s important to protect individuals’ identifications and efforts should be put to anonymize the collected datasets.
• Leverage techniques from artificial intelligence (AI) and machine learning (ML) to understand, correlate and draw findings from the disaster related data.
• Analysis from processed disasters information can help to identify the most effective strategies to respond future disasters.
• Ensure data consistency, accuracy and completeness for decision making processes.
• Need to investigate data mining challenges as well for disaster management.
• Security as well as privacy issues in data transmission and storage also need to be under constant investigation.

Conclusion:

Big data has a significant role in all phases of disaster management. Big data from sensor networks, social media, and from other sources are available and shows its usefulness in disaster management already.

These big data help policy makers and first responders to come with quick and concrete decision on the number of people affected, type and nature of the damage and where to allocate the resource. Many natural hazard forecasting systems rely on big data. Early warning system for tsunami, storm, forest fire, and flood can be more accurate and reliable from these huge volumes of data. 

Crowd-sourcing, cyber infrastructure, and cloud computing approach can be used to get required information for emergency management by analysing big data. Machine learning approach and parallel processing approach might save valuable processing time during an emergency. The big data archive can be helpful for model development and validation to ensure more efficient disaster management.

Despite having these challenges, research and data gathering on the usefulness of big data in disaster management are ongoing. The disaster managers and policy makers will get more confidence on the usefulness of big data in disaster management.