水电能源科学2024,Vol.42Issue(10) :11-15.DOI:10.20040/j.cnki.1000-7709.2024.20232033

基于深度学习模型的中小河流洪水模拟

Flood Simulation of Small and Medium-sized Rivers Based on Deep Learning Model

张景帅 胡彩虹
水电能源科学2024,Vol.42Issue(10) :11-15.DOI:10.20040/j.cnki.1000-7709.2024.20232033

基于深度学习模型的中小河流洪水模拟

Flood Simulation of Small and Medium-sized Rivers Based on Deep Learning Model

张景帅 1胡彩虹2
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作者信息

  • 1. 河海大学水文水资源学院,江苏 南京 210098
  • 2. 郑州大学黄河实验室,河南 郑州 450001
  • 折叠

摘要

为了研究LSTM模型在我国众多流域中的适用性,在海河、黄河、淮河、长江流域共选取了9个小流域作为研究区域,训练并验证了LSTM在这些流域中的模拟精度.结果表明,随着预见期的增加,LSTM模型模拟效果呈下降趋势,在预见期为1~6 h时可以得到良好的模拟结果,在预见期大于6 h时,模拟结果较差;随着神经元数量和迭代步数的增加,LSTM模型的模拟效果有所提高,超过一定组合后,模拟效果变化不明显.研究结果可为中小流域的洪水预报提供支持.

Abstract

In order to study the applicability of the LSTM model in many basins,a total of 9 small basins were select-ed as the research area in the Hai River,Yellow River,Huai River,and Yangtze River basins,and the simulation accura-cy of LSTM was trained and verified in these basins.The results indicate that as the forecast periods increase,the simula-tion performance of the LSTM model shows a decreasing trend.Good simulation results can be obtained when the fore-cast periods are 1-6 hours,but the simulation results are poor when the forecast periods are greater than 6 hours;As the number of neurons and iteration steps increase,the simulation effect of the LSTM model improves.However,after ex-ceeding a certain combination,the simulation effect does not change obviously.The research results can provide support for flood forecasting in small and medium-sized basins.

关键词

深度学习/LSTM/中小河流/洪水模拟

Key words

deep learning/LSTM/small and medium-sized rivers/flood simulation

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出版年

2024
水电能源科学
中国水力发电工程学会 华中科技大学 武汉国测三联水电设备有限公司

水电能源科学

CSTPCD北大核心
影响因子:0.525
ISSN:1000-7709
参考文献量7
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