| 改进的KNN实时校正方法在山区中小流域的应用 |
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| 引用本文： | 霍文博,高源,李致家,金双彦,杨明祥.改进的KNN实时校正方法在山区中小流域的应用[J].河海大学学报(自然科学版),2023,51(4):27-32. |
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| 作者姓名： | 霍文博 高源 李致家 金双彦 杨明祥 |
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| 作者单位： | 黄河水利委员会水文局,河南 郑州450004;中国水利水电科学研究院水资源研究所,北京100038;黄河水利委员会山东水文水资源局,山东 济南250100;河海大学水文水资源学院,江苏 南京210098 |
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| 基金项目： | 第二次青藏高原综合科学考察研究资助项目(2019QZKK0203)；国家重点研发计划项目(2018YFC1508100)；流域水循环模拟与调控国家重点实验室自由探索课题资助项目(SKL2020TS01) |
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| 摘 要： | 为提高山区中小流域实时洪水预报精度,提出了一种基于历史洪水学习的KNN实时校正方法(KNN-H法),并选择陕北黄土高原地区2个山区中小流域为研究区域,将其与传统KNN法和AR法进行对比,验证该方法的校正效果。结果表明:KNN法和KNN-H法的校正精度总体高于AR法；KNN法和AR法不能有效降低预报结果的峰现时间误差,而KNN-H法校正结果峰现时间误差比校正前有明显降低；KNN-H法通过对历史洪水预报误差的学习,可有效解决KNN法在实时校正中因为预热期资料不足导致的校正精度不高问题；当预报洪水过程处于涨洪或退水阶段时,KNN-H法能够快速定位到历史洪水的相同阶段,分析历史预报误差后迅速对当前预报值做出校正；总体上KNN-H法校正精度高于传统KNN法。
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| 关 键 词： | KNN实时校正法 洪水预报 山区中小流域 超渗产流模型 陕北地区 |
| 收稿时间： | 2022/6/28 0:00:00 |
Application of improved KNN real-time correction method in small and medium-sized basins in mountainous areas |
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| HUO Wenbo,GAO Yuan,LI Zhiji,JIN Shuangyan,YANG Mingxiang.Application of improved KNN real-time correction method in small and medium-sized basins in mountainous areas[J].Journal of Hohai University (Natural Sciences ),2023,51(4):27-32. |
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| Authors: | HUO Wenbo GAO Yuan LI Zhiji JIN Shuangyan YANG Mingxiang |
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| Affiliation: | Hydrology Bureau of Yellow River Conservancy Commission, Zhengzhou 450004, China;Department of Water Conservancys, China Institute of Water Conservancy and Hydropower Research, Beijing 100038, China;Shandong Hydrology and Water Conservancys Bureau of YRCC, Jinan 250100, China;College of Hydrology and Water Conservancys, Hohai University, Nanjing 210098, China |
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| Abstract: | To improve the accuracy of real-time flood forecasting in small and medium-sized basin in mountainous areas, a KNN real-time correction method based on the historical flood learning (KNN-H) was proposed and applied to two small basins in mountainous area in the Loess Plateau of Northern Shaanxi Province to test its performance. The proposed method was compared with the traditional KNN and AR methods. The results show that the correction accuracy of the KNN and KNN-H method is higher than that of the AR method and the traditional KNN and AR method cannot effectively reduce the peak time error of the forecasts, while the KNN-H method can reduce the peak time error. The correction accuracy of KNN is not high due to insufficient data in preheating period, while the KNN-H method effectively solves this problem by learning the historical flood forecast error. When the forecasted flood process is in the flood rising or falling stage, the KNN-H can locate the same stage of historical flood quickly and correct the current forecast value after analyzing the historical forecast error. In general, the correction accuracy of the KNN-H method is higher than that of the traditional KNN method. |
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| Keywords: | KNN real-time correction method flood forecasting small and medium-sized basins in mountainous areas infiltrated-excess runoff model Northern Shaanxi |
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