Climate Change Data Portal
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). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。