将动态排水监督纳入深度学习,以准确实时模拟城市地区的洪水
Hancheng Ren1, Bo Pang1, Gang Zhao2
1College of Water Sciences, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing, China.
Water research
|November 29, 2024
概括
城市洪水是一个重大挑战. 一种新的城市排水监督洪水模型 (UDFM) 通过整合排水系统和地表淹水,准确模拟城市洪水,改善风险管理的预测.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 城市规划 城市规划
背景情况:
- 由于复杂的排水系统和异质的城市表面,城市洪水是一个日益严重的全球问题.
- 在城市地区准确和快速模拟洪水是具有挑战性的,阻碍了有效的风险管理.
研究的目的:
- 提出一种新的城市排水监督洪水模型 (UDFM),用于高分辨率的时空空间城市洪水模拟.
- 通过整合排水系统动态来提高城市洪水预测的准确性和效率.
主要方法:
- 将城市洪水流程脱为排水路线和地表淹水.
- 开发一个混合模块,将深度学习和维度减小结合起来,用于1D到2D的洪水过程转换.
- 在UDFM框架内使用物理和深度学习排水模型.
主要成果:
- UDFM准确地代表了排水系统在城市洪水动态中的作用,改善了洪水深度和速度的预测.
- 在深,UDFM深度学习展示了实时预测能力和高精度,超过了现有模型 (RSM,LFM).
- 纳什效率系数提高了0.112 (RSM) 和0.251 (LFM),突出了优越的流速模拟.
结论:
- 排水系统的溢出对于精确的城市表面洪水模拟至关重要.
- 在没有新的物理结构的情况下,UDFM为城市洪水建模提供了更高的准确性,灵活性和可解释性.
- 这种等级替代模型为城市洪水预警和风险管理系统提供了宝贵的见解.
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