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| DOI | 10.1007/s00477-023-02640-9 |
| Building information modeling integrated with environmental flood hazard to assess the building vulnerability to flash floods | |
| 发表日期 | 2024 |
| ISSN | 1436-3240 |
| EISSN | 1436-3259 |
| 起始页码 | 38 |
| 结束页码 | 2 |
| 卷号 | 38期号:2 |
| 英文摘要 | Recently, significant global regions have encountered unparalleled consequences arising from climate change, notably manifesting in the heightened intensity of flash floods which presents a formidable and complex challenge for numerous governments globally. The main goal of this research is quantifying the Building Vulnerability Index (BVI), a metric devised to assess the susceptibility of buildings to flash floods. This study introduces a two-faceted index focusing on Intrinsic Vulnerability (IV) and Environmental Flood Hazard (EFH). The IV pertains to intrinsic characteristics of structures that influence their ability to withstand and recover from flash floods, whereas EFH involves the evaluation of potential hazards from external flooding events through the utilization of eleven environmental parameters. This assessment employs machine learning, specifically an Artificial Neural Network with Multilayer Perceptron architecture, functioning as both classifier and regressor models. The EFH classifies 40,521 structures according to five levels of vulnerability, with level one indicating a very low hazard and level 5 signifying a very high hazard. The majority of buildings, approximately 90%, fall into the categories of levels 2 and 3, indicating low and moderate flood hazard. Receiver Operating Characteristic (ROC) analysis validates high accuracy of the two adopted machine learning approaches after estimating area under curve as 94.27% and 95.22% for classifier and regressor models respectively. Consequentially, calculation of BVI across 36 structures underscores a spectrum of susceptibility, ranging from 0.471 to 0.795. Notably, this investigation introduces a novel approach by amalgamating building-specific data with environmental flood hazard assessment, thereby enhancing the evaluation of flood vulnerability. Finally, the study successfully estimates integrated flood vulnerability and offers valuable insights for decision-makers in devising mitigation strategies. |
| 英文关键词 | Flash flood; Climate change; Machine learning; Artificial Neural Network; ROC |
| 语种 | 英语 |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources |
| WOS类目 | Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources |
| WOS记录号 | WOS:001141998600002 |
| 来源期刊 | STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289408 |
| 作者单位 | Egyptian Knowledge Bank (EKB); Egypt-Japan University of Science & Technology; Egyptian Knowledge Bank (EKB); Mansoura University; Egyptian Knowledge Bank (EKB); Suez Canal University; Egyptian Knowledge Bank (EKB); South Valley University Egypt; Tokyo Institute of Technology; Egyptian Knowledge Bank (EKB); City of Scientific Research & Technological Applications (SRTA-City) |
| 推荐引用方式 GB/T 7714 | . Building information modeling integrated with environmental flood hazard to assess the building vulnerability to flash floods[J],2024,38(2). |
| APA | (2024).Building information modeling integrated with environmental flood hazard to assess the building vulnerability to flash floods.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,38(2). |
| MLA | "Building information modeling integrated with environmental flood hazard to assess the building vulnerability to flash floods".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 38.2(2024). |
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