在长期不确定性下,城市河流中藻类繁殖的基于深度学习的最佳适应性调节途径
Changqing Xu1, Tianyu Jia1, Te Xu2
1School of Economics, Beijing Institute of Technology, Beijing 100081, China; Digital Economy and Policy Intelligentization Key Laboratory of Ministry of Industry and Information Technology, Beijing 100081, China.
Water research
|October 16, 2025
概括
控制城市水中的藻类繁殖需要适应气候变化的战略. 绿色基础设施是减轻气温上升和保护水生生态系统的最具成本效益的解决方案.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 适应气候变化 适应气候变化
背景情况:
- 控制藻类繁殖是城市水资源管理的一个重大挑战,气候变化加剧了这一问题.
- 现有的战略往往缺乏长期适应气候不确定性的能力.
- 有效的管理需要整合预测建模和适应性规划.
研究的目的:
- 通过动态适应性政策路径 (DAPP) 框架加强城市藻类繁殖管理.
- 开发一个决策支持系统,集成系统故障信号,深度学习和优化.
- 在气候不确定性下确定城市水资源管理的成本有效的适应性策略.
主要方法:
- 应用了DAPP框架,包括8个气候和19个监管场景,用于中国苏州的案例研究.
- 开发了一个深度学习替代模型 (AdaBoost, Bagging) 用于快速藻类繁殖模拟 (R2>0.95).
- 利用NSGA-II优化算法来平衡环境和经济因素,考虑基础设施限制.
主要成果:
- 气温上升对城市水生生态系统构成最严重的威胁;降水和辐射影响较小.
- 绿色基础设施 (GI) 成为减轻热影响的最经济可行的战略.
- 开发的代用模型实现了高精度和显著减少模拟时间.
结论:
- 该DAPP框架,加上先进的建模和优化,为弹性城市水资源管理提供了一个可扩展的解决方案.
- 专注于减温的适应性战略,特别是通过绿色基础设施,对于未来的水安全至关重要.
- 这项研究提供了一个强大的决策支持工具,用于在水资源规划中应对气候不确定性.
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