创建和选择替代洪水淹没模型的培训活动
Niels Fraehr1, Quan J Wang1, Wenyan Wu1
1Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, 3010, Australia.
Journal of environmental management
|December 7, 2024
概括
一种新方法有效地选择洪水事件用于训练预测模型,大大降低了计算成本. 这种方法确保了精确的洪水淹没预测与较少的模拟,改善防灾准备.
科学领域:
- 环境科学环境科学
- 计算流体动力学 计算流体动力学
- 机器学习是机器学习.
背景情况:
- 准确的洪水洪水预测对于减轻洪水事件影响至关重要.
- 数据驱动的替代模型提供快速的洪水预测,但需要广泛的训练数据.
- 高准确度的水力动力学模型能产生准确的数据,但计算密集,限制了训练数据集的大小.
研究的目的:
- 引入一种新的,系统的方法来选择代表性的洪水事件来训练替代洪水淹没模型.
- 为了降低与生成替代模型培训数据相关的计算成本.
- 为了提高替代洪水淹没模型的稳定性和准确性.
主要方法:
- 一个计算效率高的低保真水力学模型会产生大量的潜在洪水事件.
- 培训活动的选择是基于来自候选活动的模拟时空淹水深度.
- 替代模型使用所选事件进行训练,并在各种案例研究中对未见的洪水事件进行验证.
主要成果:
- 用拟议的事件选择方法训练的替代模型在未见事件上表现出强的性能,RMSE < 0.23 m.
- 用选定的事件训练的模型的准确性与用所有可用的候选事件训练的模型相比较.
- 该方法通过将高保真度模拟的需求降至最低,将训练数据生成的计算成本降低高达97%.
结论:
- 提出的系统方法有效地选择了信息丰富的洪水事件,使得准确和计算效率高的替代洪水模型的开发成为可能.
- 这种方法显著降低了数据生成的计算负担,使先进的洪水预测更容易获得.
- 该方法在洪水动态之外的其他科学和工程领域的替代模型开发中具有广泛的适用性.
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