在风险框架内使用人工智能对热浪的脆弱性评估
Jean-Nicolas Côté1, Mickaël Germain1, Elisabeth Levac2
1Department of Applied Geomatics, Université de Sherbrooke, 2500, boulevard de l'Université, Sherbrooke J1K 2R1, Quebec, Canada.
The Science of the total environment
|December 20, 2023
概括
目前的气候变化适应战略不足. 这项研究引入了一个新的数据驱动的风险框架,用于脆弱性评估,改善对北克热浪等气候影响的资源配置.
科学领域:
- 环境科学环境科学
- 适应气候变化 适应气候变化
- 公共卫生 公共卫生
背景情况:
- 现有的气候变化适应努力不足以减轻预计的影响.
- 脆弱性评估对于有效的资源分配至关重要,但当前的方法,如主要组件分析,具有局限性.
- 基于主要组件分析的评估很难转化为可行的策略.
研究的目的:
- 在风险背景下提出一个新的数据驱动的脆弱性评估框架.
- 根据IPCC第六次评估报告,澄清敏感性和适应能力的定义.
- 用加拿大北克的热浪数据和死亡率来验证拟议的框架.
主要方法:
- 制定了一个风险框架,包含了IPCC澄清的定义.
- 利用热浪数据 (2001-2018) 和北克的每日死亡率.
- 使用社会经济和环境变量来计算影响和脆弱性.
- 将AutoGluon,高斯过程和深度高斯过程进行比较,以建模影响和脆弱性.
- 使用 SHAP 值来解释模型的可解释性.
主要成果:
- 创建了一个脆弱性地图,说明北克夏季死亡率的分布.
- 高斯的过程建模对拟的框架表现出了前景.
- 证实模型结果的SHAP值与现有的漏洞知识保持一致.
- 确定了更容易受到热浪影响的特定区域.
结论:
- 拟议的数据驱动框架为脆弱性评估提供了一种更具可操作性的方法.
- 高斯过程为建模气候变化影响和脆弱性提供了合适的方法.
- 该框架有助于确定有针对性的适应干预措施的高风险地区.
- 提供了对框架的定量和质量实施的建议.
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