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| 引用本文: | 叶可佳,梁忠民,陈红雨,钱名开,胡义明,王军,李彬权.区域化长短期记忆神经网络(LSTM)洪水预报模型研究.湖泊科学,2025,37(2):651-659. DOI:10.18307/2025.0245 |
| Ye Kejia,Liang Zhongmin,Chen Hongyu,Qian Mingkai,Hu Yiming,Wang Jun,Li Binquan.Research on regional long-short term memory neural network(LSTM) flood forecasting model. J. Lake Sci.2025,37(2):651-659. DOI:10.18307/2025.0245 |
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| 区域化长短期记忆神经网络(LSTM)洪水预报模型研究 |
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叶可佳1，梁忠民1，陈红雨2，钱名开1，2，胡义明1，王军1，李彬权1
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1.河海大学水文水资源学院，南京 210098 ；2.淮河水利委员会水文局（信息中心），蚌埠 233001
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| 摘要: |
| 针对水文资料缺乏流域机器学习模型建模困难的问题，本文提出了基于长短期记忆神经网络（LSTM）的区域化洪水预报方法。对水文气候相似区内各流域的水文及地形地貌特征数据进行归一化处理，以消除局地因素的影响，从而构建相似区内建模统一数据集，扩大样本数量，为建立乏资料流域洪水预报模型提供了可能。本文选择胶东半岛作为研究区进行应用研究。为验证区域化模型在不同场景中的应用效果，设计了预报流域数据不参与建模，而仅根据区域内其他流域资料建模（区域化模型Ⅰ），以及预报流域的部分数据参与建模（区域化模型Ⅱ）两种情景；此外，选取仅根据预报流域数据训练的单流域模型作为基准模型进行对比分析。结果表明，对本次研究的水文资料短缺流域，两种区域化模型均取得了较好效果，且都优于单流域模型。相较而言，考虑了预报流域数据的区域化模型精度更高，说明在区域化LSTM构建中融入预报流域的数据，可进一步提升区域化模型的精度。研究成果可为乏资料地区的洪水预报提供参考。 |
| 关键词: 长短期记忆神经网络 洪水预报 区域化模型 水文气候相似区 乏资料流域 |
| DOI：10.18307/2025.0245 |
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| 基金项目:国家自然科学基金项目（52379007，42371045）；浙江省水利厅科技计划项目（RC2214）；水利部重大科技项目（SKR-2022032）联合资助 |
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| Research on regional long-short term memory neural network(LSTM) flood forecasting model |
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Ye Kejia1，Liang Zhongmin1，Chen Hongyu2，Qian Mingkai1，2，Hu Yiming1，Wang Jun1，Li Binquan1
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1.College of Hydrology and Water Resources, Hohai University, Nanjing 210098 , P.R.China ；2.Hydrological Bureau (Information Center) of Huaihe River Water Resources Commission, Bengbu 233001 , P.R.China
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| Abstract: |
| Aiming at the difficulty of hydrological modeling in hydrological data-scarce catchments, a regional flood forecasting method based on long-short term memory neural network (LSTM) was proposed in this paper. Through scalarizing the data of topographic and geomorphological factors of each catchment in the same hydroclimatic similarity zone, the influence of local factors could be eliminated. On this basis, a unified modeling dataset in a similarity zone was constructed and the sample size was effectively expanded, which provided a possibility for the establishment of flood forecasting models in the data-sparse catchments. The Jiaodong Peninsula was selected as the study area in this study. In order to verify the application effect of the regional model in different scenarios, two regional modeling schemes were designed in this paper. To be specific, the first scheme was to construct a regional model based on the data of other basins in the similarity zone, without the participation of the data of the forecast basin (regional model Ⅰ). The second scheme was to construct a regional model with the data both from the forecast basin and other basins in the same region (regional model Ⅱ). In addition, the “single-basin model”, which was trained only with forecast basin's data, was selected as a benchmark model. Results indicated that, for data-scarce basins in this study, both regional models exhibited high accuracy and were superior to single-basin model. Comparatively, the regional model that incorporated data from the forecast basin outperformed the other regional model, suggesting that the accuracy of the regional model could be further improved if the data of the forecast basin was incorporated into model construction. The study can provide a reference for flood forecasting in data-scarce catchments. |
| Key words: Long-short term memory neural network flood forecasting regional model hydroclimatic similarity zone data-scarce catchments |
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