使用监督机器学习发现影响地震保险采用的因素
John N Ng'ombe1, Kwabena Nyarko Addai2, Agness Mzyece3
1Department of Agribusiness, Applied Economics and Agriscience Education, North Carolina A&T State University, Greensboro, NC, 27411, USA. jngombe@ncat.edu.
Scientific reports
|December 3, 2023
概括
了解地震保险的采用对于环境风险管理至关重要. 年龄,性别和过去的地震经历等因素影响购买决策,机器学习模型显示出预测能力.
科学领域:
- 环境科学 环境科学
- 风险管理 风险管理
- 地质科学 地质科学
背景情况:
- 自然灾害对全球公共安全构成越来越大的威胁.
- 地震保险是环境风险管理的关键工具,特别是与废水注入 (2011-2020年) 相关的俄克拉荷马州的地震性.
研究的目的:
- 确定影响俄克拉荷马州地震保险采购的因素.
- 用监督机器学习来预测可能购买地震保险的个人.
主要方法:
- 在812名俄克拉荷马州居民中进行了调查.
- 监督机器学习分类器的应用:logit,,Lasso,决策树和随机森林.
主要成果:
- 人口统计因素 (年龄,男性性别,种族,种族),租物业居住地,俄克拉荷马州长期居住地,以及先前的地震经验显著影响保险吸收.
- 决策树和随机森林显示出强大的预测能力,随机森林显示出卓越的精度和稳定性.
结论:
- 保险是环境风险管理的一个关键工具.
- 需要提高对地震保险的认识和教育.
- 监督机器学习,特别是随机森林,对于地震保险建模和类似的分类任务是有效的.
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