Improved Disaster Preparedness Using (Big) Data Analytics

We are in the eye of the storm of rapidly evolving challenges, such as the refugee crisis, the climate change and natural or man-made disasters, just to name a few. For sure, we cannot be fully prepared for all the crises that follow these incidents and impact public safety but it is our responsibility to ensure that a well-functioning disaster preparedness system is in place to address the needs of vulnerable people directly affected by all these unanticipated events.
Within IN-PREP framework, we are developing an IT-based platform that facilitates the planning and preparation for transboundary crisis management for several natural and man-made disasters such as terrorist attacks, earthquakes and large forest fires. One of the main modules of the platform is dedicated to the data analysis process not only of historical data but also of data coming from diverse sources such as Geographical Information Systems (GIS), Global Positioning Systems (GPS), social and environmental monitoring sensors.
The seamless integration of these data streams, along with the processing paradigm of the Hadoop ecosystem, can support data processing and storage for effective disaster preparedness. In particular, (Big) Data Analytics can be used in the preparedness phase of disaster management for disaster monitoring, detection, prediction and forecasting. Especially for the latter case, predictive analytics can be a powerful tool for natural disaster management since natural disasters are difficult to forecast mainly due to the complexity associated with the physical phenomena and the variability of the involved parameters. For all these activities regarding data analysis, we rely on the EXUS Analytics Framework (EAF). EAF is the result of EXUS’ long-term product evolution that leverage in-house expertise in conjunction with a strong portfolio of research and innovation activities. The driving idea behind the design and development of EAF is the seamless facilitation of analytics functions for both batch and streaming data, without imposing strong requirements on expertise and specialization. EAF comprises of several cloud-based services and components that support intensive processing and large-scale activities, hiding (if needed) complex operations by the end-user.
Concluding, the most important thing in a crisis situation is to show up and not make a show. In order to do so, you need to be certain that you have done everything humanly possible to be prepared for disasters and their consequences to the society. (Big) Data Analytics is an effective tool that can assist safety leaders to recognize, gain insight, visualize and act on disaster incidents instead of simply reacting. At the same time, intelligent conclusions about the potential correlation of events can help them optimize the procedural operations and thus respond to unforeseen, sudden and overwhelming events with competence and decisiveness.

 

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