一个基于水文相似性的参数动态匹配框架:用于增强实时洪水预测的应用
Hongshi Wu1, Peng Shi2, Simin Qu1
1College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
The Science of the total environment
|October 13, 2023
概括
本研究引入了一种混合框架,用于在不断变化的条件下精确的实时洪水预测,使用从历史数据中获得的每小时时间变量参数. 与传统模型相比,新方法提高了准确性和可靠性.
科学领域:
- 水文与水资源工程 水文与水资源工程
- 环境科学 环境科学
- 数据科学和机器学习
背景情况:
- 传统的水文模型假定静态,时间不变的参数,由于气候变化和人类干预,这一点越来越值得怀疑.
- 机器学习 (ML) 为分析广泛的水文气象数据提供了强大的工具,以识别复杂的模式.
- 需要先进的洪水预测方法,能够适应非静止的水文条件.
研究的目的:
- 提出和评估一个混合框架 (HSPDM) 以提高实时洪水预报的精度.
- 使用ML技术动态检索时间变量的水文模型参数,以提高预测准确度.
- 评估框架的性能与传统的时间不变参数模型和概率预测方案相比.
主要方法:
- 开发了一个混合框架 (HSPDM),集成了ML技术,如k-means,K-Nearest Neighbor (KNN) 和基于嵌入的后续匹配 (EBSM).
- 从历史上类似的洪水事件中动态提取每小时时间变量的水文模型参数.
- 对比了三个预测方案:传统的时间不变参数,每小时的时间变量参数和概率预测.
主要成果:
- 在HSPDM框架成功地识别了连续洪水后续的高精度和可接受的计算时间.
- 每小时的时间变量参数方案显示出优异的预测准确性,优于传统的时间不变参数方案.
- 时间变量方案的预测与概率预测一致,甚至超过了概率预测,表明可靠性提高.
结论:
- 拟议的混合框架通过结合每小时的时间变量参数来推进实时洪水预测,改善对非静止条件下的模型行为的理解.
- 这种方法为操作性防洪和减灾提供了可靠的替代方案.
- 该研究强调了在水文建模中基于ML的动态参数化的潜力.
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