Climate Change Data Portal
| DOI | 10.1007/s13753-024-00541-1 |
| Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods | |
| 发表日期 | 2024 |
| ISSN | 2095-0055 |
| EISSN | 2192-6395 |
| 起始页码 | 15 |
| 结束页码 | 1 |
| 卷号 | 15期号:1 |
| 英文摘要 | Facing the escalating effects of climate change, it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management. Current studies in this area often have relied on psychology-driven linear models, which frequently exhibited limitations in practice. The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors, compared to existing models that mainly rely on psychological factors. An enhanced logistic regression model (that is, an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions (that is, univariate and bivariate threshold effects). Specifically, nonlinearity and interaction detection were enabled by low-depth decision trees, which offer transparent model structure and robustness. A survey dataset collected in the aftermath of Hurricanes Katrina and Rita, two of the most intense tropical storms of the last two decades, was employed to test the new methodology. The findings show that, when predicting the households' evacuation decisions, the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability. This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner. |
| 英文关键词 | Artificial Intelligence (AI); Decision-making modeling; Hurricane evacuation; Interpretable machine learning; Nonlinearity and interaction detection |
| 语种 | 英语 |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences ; Water Resources |
| WOS类目 | Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources |
| WOS记录号 | WOS:001174163100001 |
| 来源期刊 | INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/296554 |
| 作者单位 | State University System of Florida; University of Florida; Jacksonville State University |
| 推荐引用方式 GB/T 7714 | . Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods[J],2024,15(1). |
| APA | (2024).Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods.INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE,15(1). |
| MLA | "Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods".INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE 15.1(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
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。
