10th Annual BayesiaLab Conference
24 October 2022 – h 14:30 pm CET
Zoom Virtual Event | INFO
Speaker: Pham Hung Vuong
Extreme weather and climate related events, from river flooding to droughts and tropical cyclones, are likely to become both more severe and more frequent in the coming decades, and the damages caused by these events will be felt across all sectors of society. In the face of this threat, policy- and decision-makers are increasingly calling for new approaches and tools to support risk management and climate adaptation pathways that can capture the full extent of the impacts. In this context, Bayesian Network (BN) stands as a novel and powerful approach for the capturing and modelling of multi-risk against future ‘what-if’ scenarios.
Building on a risk-based conceptual framework, several BN models were developed, trained, and validated by expert judgment and data-based driven to support multi-risk scenario analysis with various aims such as multi-sectoral flooding damages, marine cumulative impacts, ecosystem services assessment in different domains (e.g., freshwater, marine and coastal, agriculture and industry). A major advantage across these applications lies in the possibility to combine heterogeneous data from multiple sources and across different domains, which is vital in environmental risk assessment.
The outcome of these applications represents a valuable support for disaster risk management and reduction actions against climate change and extreme events, enabling better informed decision making. Furthermore, more ambitious development could involve the spatialization of the output of the model with user-friendly interface, building on the GIS-based structure of the training dataset, to assist policy- and decision-makers to use the results of these applications to prioritize more efficiently plans for Disaster Risk Management and Climate Change Adaptation.