This research direction includes a unified multi-layer framework that encapsulates machine learning techniques in a specific assessment process for analysing and fusing dynamically heterogeneous information obtained from various sources. For example, in the disaster management sector, the solutions that we design and develop in the research direction aim
- to classify the severity of a crisis in pre-emergency as well as emergency phase, enhancing the preparedness and response of civil protection authorities, agencies and response services to tackle efficiently a hazardous event
- to facilitate the communication between stakeholders and user interfaces, our activities aim to develop comprehensive and interactive visualisations which will encapsulate high-quality information about hazards, exposure, vulnerability, risks
- to maximise usability, user interfaces will be based on visual analytics creating a strong synergy between the end-user, the analysis methods (e.g., machine learning), and effective visualisations that aggregate and map the risk assessment results in an easy way to non-experts.