Decision Support System Platform for Risk Assessment and Emergency Response Management based on an Integrated, Visually Enhanced Big Data-

Currently, there are still limitations to the near real-time collection, exchange, analysis and interpretation of both static and dynamic datasets (either directly from in-situ sensors/continuous monitoring systems) of the various emergency response management stakeholders, especially for use in risk assessment simulation models. At present, emergency event data feeds (such as road closures) and in-situ sensor data feeds (such as meteorological observation) are shared in near real-time. Due to the apparent complexity of these assessment models for various natural hazard types, decision makers tend to discard information that seems to increase the complications pertaining to the work they already have to deal with. Regardless of the sophistication of these models in terms of detail, physical process and visualisation means, there are delays in providing stakeholders with this information due to inflexibility and long computation times as most models are based on static dataset options that do not effectively recalculate emergency hazard scenarios based on continuously evolving (and dynamic) ?near real-time? datasets. This doesn?t match the frequency and fluidity of the decision-making process and, together with delays in the information exchange among the various stakeholder organisations, can cause the models? information to quickly become outdated and hence not as useful anymore. Therefore, this ?simplification strategy? approach has not delivered the most effective response/recovery strategies, sometimes leading to the implementation of wrong or unnecessary measures, e.g. the evacuation of areas that are not at risk or the ineffective use of resources that would be better served utilised when being concentrated in areas with the most pressing needs.