Data from Venice Provide New Insights into Risk Management (A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis): Risk Management

2022 FEB 18 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Researchers detail new data in risk management. According to news originating from Venice, Italy, by NewsRx correspondents, research stated, “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.”

Our news reporters obtained a quote from the research from Department of Environmental Sciences: “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 frame, a GIS-based Bayesian Network (BN) approach is presented for the capturing and modelling of multi-sectoral flooding damages against future ‘what-if’ scenarios. Building on a risk-based conceptual framework, the BN model was trained and validated by exploiting data collected from the 2014 Secchia River flooding event, as well as other contextual variables. Moreover, a novel approach to defining the structure of the BN was performed, reconfiguring the model according to expert judgment and data-based validation. The model showed a good predictive capacity for damages in the agricultural, industrial and residential sectors, predicting the severity of damages with a classification accuracy of about 60% for each of these assessment endpoints. ‘What-if’ scenario analysis was performed to understand the potential impacts of future changes in i) land use patterns and ii) increasing flood depths resulting from more severe flood events.”

According to the news editors, the research concluded: “The output of the model showed a rising probability of experiencing high monetary damages under both scenarios. In spite of constraints within the case study dataset, the results of the appraisal show good promise, and together with the designed BN model itself represent a valuable support for disaster risk management and reduction actions against extreme river flooding events, enabling better informed decision making.”

For more information on this research see: A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis. Climate Risk Management, 2022,35():100410. (Climate Risk Management - http://www.journals.elsevier.com/climate-risk-management/). The publisher for Climate Risk Management is Elsevier.

A free version of this journal article is available at https://doi.org/10.1016/j.crm.2022.100410.

Our news journalists report that more information may be obtained by contacting Remi Harris, Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, I-30170 Venice, Italy. Additional authors for this research include Elisa Furlan, Hung Vuong Pham, Silvia Torresan, Jaroslav Mysiak, Andrea Critto.

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