基于强化学习的气候变化适应适应策略:沿海洪水风险管理应用
Kairui Feng1,2, Ning Lin2, Robert E Kopp3,4
1National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai 201210, China.
概括
强化学习 (RL) 通过从海平面上升数据系统地学习来优化气候适应战略. 与传统方法相比,这种机器学习方法显著降低了沿海防洪系统的成本和风险管理.
科学领域:
- 气候科学 气候科学
- 机器学习 机器学习
- 环境政策 环境政策
背景情况:
- 传统的气候适应模型往往忽视了决策者的学习能力.
- 适应性决策需要根据新的环境信息不断更新.
研究的目的:
- 调查强化学习 (RL) 用于建模和告知适应性气候决策.
- 评估RL在沿海洪水风险减轻战略中的有效性.
主要方法:
- 利用强化学习 (RL),一种用于优化动态决策的机器学习技术.
- 模拟了纽约市曼哈顿的适应性海战略,结合了海平面上升的观测.
- 在不同的排放场景下,将RL衍生策略与传统方法进行比较 (SSP2-4.5,SSP5-8.5).
主要成果:
- 在适应性海墙方面,RL战略将预期净成本降低了6-36% (SSP2-4.5) 和9-77% (SSP5-8.5).
- 在整合住宿和撤退政策时,RL实现了进一步的成本削减 (5-15%).
- 在管理尾部风险和减轻气候错误信息造成的损失方面,RL表现出卓越的表现.
结论:
- 强化学习为加强气候适应战略提供了一个强大的框架.
- 系统的学习和更新对于应对气候变化的极端和不确定性至关重要.
- RL提供了一种灵活和有效的方法,用于复杂的,动态的政策设计在适应.
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