气候变化领域集成服务门户(CCPortal): A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis

CCPortal

DOI	10.1016/j.crm.2022.100410
	A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis
	Harris R.; Furlan E.; Pham H.V.; Torresan S.; Mysiak J.; Critto A.
发表日期	2022
ISSN	2212-0963
卷号	35
英文摘要	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 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. 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. © 2022 The Author(s)
英文关键词	Climate adaptation; Flood risk assessment; Machine Learning; Secchia river; Sensitivity analysis
语种	英语
来源期刊	Climate Risk Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/256081
作者单位	Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, Venice, I-30170, Italy; Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, Lecce, I-73100, Italy
推荐引用方式 GB/T 7714	Harris R.,Furlan E.,Pham H.V.,et al. A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis[J],2022,35.
APA	Harris R.,Furlan E.,Pham H.V.,Torresan S.,Mysiak J.,&Critto A..(2022).A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis.Climate Risk Management,35.
MLA	Harris R.,et al."A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis".Climate Risk Management 35(2022).