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使用机器学习通知风险热点和降雨风暴的关键缓解:来自268个中国城市的证据
Litiao Hu1, Shudan Deng1, Jixiang Ma1
1State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
Environmental science & technology
|January 17, 2025
概括
机器学习模型确定了影响268个中国城市的关键暴雨值. 调查结果指导城市规划,以应对气候变化和可持续发展目标.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 气候变化加剧了暴雨,对城市可持续性和气候行动构成风险.
- 城市洪水威胁着基础设施和社会福祉,需要积极的风险评估.
研究的目的:
- 开发一种机器学习框架,用于评估中国268个城市的暴雨风险.
- 确定关键的缓解策略,以提高城市对极端降雨事件的抵御力.
主要方法:
- 利用基于机器学习的框架,结合夜间光线数据,气象,社会经济和基础设施因素.
- 使用因果森林 (CF) 用于风险值识别和随机森林与SHAP (RF-SHAP) 用于缓解策略分析.
- 应用固定效应 (FE) 模型用于分析不同季节暴雨的时间影响.
主要成果:
- 确定每月150和250毫米的暴雨总量为关键值,在中国东部和中北部产生重大影响.
- 突出有效的缓解措施:开发了排水系统,桥梁,扩大了道路网络和水.
- 确定最大的负面影响发生在春季 (四月),其次是秋季和冬季,对于特定的降雨门.
结论:
- 综合机器学习框架为基于证据的城市减灾战略提供了可靠的估计.
- 调查结果有助于实现可持续发展目标11 (可持续城市) 和13 (气候行动).
- 这种方法增强了城市适应能力和应对气候变化引起的极端天气事件的弹性.
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