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FloodCastBench:用于洪水建模和预测的大规模数据集和基础模型
Qingsong Xu1,2, Yilei Shi3, Jie Zhao1
1Data Science in Earth Observation, Technical University of Munich, Munich, 80333, Germany.
Scientific data
|March 13, 2025
概括
本研究介绍了FloodCastBench,这是一个用于机器学习 (ML) 洪水预测的新数据集. 它提供来自重大洪水事件的动态过程数据,以提高ML模型的准确性和可靠性.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 数据科学数据科学数据科学
背景情况:
- 有效的洪水预报对于灾害管理和公共安全至关重要.
- 目前的洪水数据集主要描述静态洪水事件,缺乏用于高级机器学习 (ML) 模型所必需的动态过程数据.
- 需要全面,动态的数据集来推进基于ML的洪水预测.
研究的目的:
- 引入FloodCastBench,这是一个专门为基于ML的洪水建模和预测而设计的新型数据集.
- 从水力动力学建模中获得的高分辨率的时空空间动态洪水过程数据.
- 使用FloodCastBench数据集建立神经洪水预测的基准.
主要方法:
- 数据采集涉及准备输入数据 (地形,土地使用,降雨量) 和收集洪水测量 (基于SAR的地图,调查的轮).
- 使用有限差数值解决方案生成高分辨率 (30米空间,300秒时间) 的动态洪水过程.
- 洪水测量数据被用于水力动力学模型校准和洪水地图的验证.
主要成果:
- FloodCastBench提供适合ML应用的低保真度和高保真度洪水预测数据集.
- 该数据集涵盖了四个主要的洪水事件:巴基斯坦2022年,英国2015年,澳大利亚2022年和莫桑比克2019年.
- 基础模型的基准表明了数据集对时空,跨区域和缩小规模的洪水预测的有效性.
结论:
- 通过提供动态的高分辨率数据,FloodCastBench显著提高了基于ML的洪水预测能力.
- 数据集和基准模型验证了ML在各种场景中改善洪水预测的潜力.
- 该资源有助于在开发更准确,更可靠的洪水预测系统方面取得进展.
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