使用支持矢量机进行实时洪水深度预测的快速模拟
Beom-Jin Kim1, Minkyu Kim1, Jaehwan Yoo2
1Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea.
Scientific reports
|August 29, 2025
概括
这项研究使用支持矢量机 (SVM) 开发了一种快速洪水深度预测模型,用于易受洪水影响的城市地区. 用物理模拟数据进行训练的SVM模型为及时应对灾害提供了快速可靠的预测.
科学领域:
- 环境科学
- 水文学
- 城市规划
背景情况:
- 气候变化加剧地方强降雨,导致严重的城市洪水.
- 传统的水力动力学模型 (SWMM,FLO-2D) 准确但计算密集,限制了实时洪水预测.
- 城市地区如江南,首尔,
研究的目的:
- 为城市地区开发快速洪水深度预测模型.
- 将机器学习与物理模拟结合起来, 以提高洪水预测.
- 支持在易受洪水影响的城市环境中及时应对灾害.
主要方法:
- 一个支持矢量机 (SVM) 模型被开发用于快速预测洪水深度.
- 使用1D-2D合水力学模拟 (SWMM-FLO-2D) 生成的数据来训练SVM模型.
- 输入变量包括1到5小时的累积降雨和排水口溢出数据.
主要成果:
- 综合SVM模型表现出高性能,其中R2=0.988,NSE=0.987,百分比差异=1.080,RMSE=0.098.
- 1D-2D水力学模型 (SWMM-FLO-2D) 与观察到的洪水记录进行了验证,其匹配率为64%.
- 与FLO-2D模拟结果相比,SVM模型准确地预测了洪水深度.
结论:
- 整合机器学习与物理模拟提供了快速可靠的洪水预测方法.
- 开发的SVM模型可以在实时的城市洪水风险管理中发挥重要作用.
- 这种方法提高了应对气候变化影响的城市地区灾害系统的效率.
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