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Demands on the disaster response capacity of the European Union are likely toincrease, as the impacts of disasters continue to grow both in size andfrequency. This has resulted in intensive research on issues concerningspatially-explicit information and modelling and their multiple sources ofuncertainty. Geospatial support is one of the forms of assistance frequentlyrequired by emergency response centres along with hazard forecast and eventmanagement assessment. Robust modelling of natural hazards requires dynamicsimulations under an array of multiple inputs from different sources.Uncertainty is associated with meteorological forecast and calibration of themodel parameters. Software uncertainty also derives from the datatransformation models (D-TM) needed for predicting hazard behaviour and itsconsequences. On the other hand, social contributions have recently beenrecognized as valuable in raw-data collection and mapping efforts traditionallydominated by professional organizations. Here an architecture overview isproposed for adaptive and robust modelling of natural hazards, following theSemantic Array Programming paradigm to also include the distributed array ofsocial contributors called Citizen Sensor in a semantically-enhanced strategyfor D-TM modelling. The modelling architecture proposes a multicriteriaapproach for assessing the array of potential impacts with qualitative rapidassessment methods based on a Partial Open Loop Feedback Control (POLFC) schemaand complementing more traditional and accurate a-posteriori assessment. Wediscuss the computational aspect of environmental risk modelling usingarray-based parallel paradigms on High Performance Computing (HPC) platforms,in order for the implications of urgency to be introduced into the systems(Urgent-HPC).

Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters