Application of Big Data and Machine Learning Approaches to Improve Decision Making During Crises

Big Data In Crisis Management

Now that we live in the age of big data, nearly every aspect of our lives is impacted. As a result of the increased interest in big data, numerous new technologies have emerged, and these technologies are expected to play a critical role in the collection, storage, and analysis of big data. According to crisis management theory, crises can be managed more effectively through the use of big data. This has created opportunities to improve and control crisis management. Scientists and analysts face a significant challenge in managing the massive amounts of data generated during disasters and crises. As a result, the role of big data in disaster and crisis management has evolved over time.

A paper by Bellmoa et al. on decision-making toward information management and crisis response proposes an essay on understanding human behaviour and managing crowds in extreme situations using big data (Belloma & Alfonso, 2016). They stated at the time that a review of crowd dynamics and safety issues demonstrated that the literature in this field can make significant contributions to the management of human crowds in emergency evacuation situations. Hayley Watson and colleagues' case study of the big data roadmap (Watson et al., 2017) corroborates findings from other studies demonstrating how big data can aid in crisis response efforts. According to the authors, research has demonstrated that expanding the use of disparate datasets, particularly big data, can improve crisis and disaster preparedness and response. Activities emerge as a significant benefit of big data in this sector due to their ability to aid in response decision-making.