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Research on LSTM-based Regionalized Flood Forecasting Model
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Cheng-lin BI1, Kuang LIU2, Zheng XIANG2, Jun WANG1, Ming-kai QIAN3, Zhong-min LIANG1
Water Resources and Power | 2023, 41(12) : 63 - 67
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Water Resources and Power | 2023, 41(12): 63-67
• HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING •
Research on LSTM-based Regionalized Flood Forecasting Model
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Cheng-lin BI1, Kuang LIU2, Zheng XIANG2, Jun WANG1, Ming-kai QIAN3, Zhong-min LIANG1
Affiliations
  • 1.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
  • 2.Hydrology Center of Shandong Province, Jinan 250000, China
  • 3.Hydrology Bureau of the Huaihe Water Conservancy Commission, Bengbu 233001, China
Published: 2023-12-25 doi: 10.20040/j.cnki.1000-7709.2023.20231253
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Limited by hydrometeorological data, flood forecasting in ungauged basins still faces challenges. Parameter regionalization is a common method to solve this problem. The machine learning model has the characteristics of simple modeling and convenient use compared with the traditional flood forecasting model. Taking the West Plain of Nansihu Lake in Shandong Province as the research area, referencing the idea of hydrological regional synthesis, this paper synthesizes the data of 40 floods in 8 watersheds from 2010 to 2021, and builds a regionalized flood forecasting model based on Long Short-Term Memory (LSTM). The results show that the regionalized flood forecasting model can simulate the actual flood process well, the relative error of flood peak in both the training set and the testing set are less than 10%, and the Nash-Sutcliffe efficiency coefficients are all greater than 0.9; In the 15 h forecast period, the regionalized flood forecasting model has higher forecasting accuracy, and when the forecast period is more than 15 h, the forecast accuracy of the model decreases.

regionalized models  /  flood forecasting  /  ungauged basins  /  LSTM
Cheng-lin BI, Kuang LIU, Zheng XIANG, Jun WANG, Ming-kai QIAN, Zhong-min LIANG. Research on LSTM-based Regionalized Flood Forecasting Model[J]. Water Resources and Power, 2023 , 41 (12) : 63 -67 . DOI: 10.20040/j.cnki.1000-7709.2023.20231253
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20231253
  • Receive Date：2023-07-27
  • Online Date：2026-01-28
  • Published：2023-12-25
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  • Received：2023-07-27
  • Revised：2023-08-28
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Affiliations
    1.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
    2.Hydrology Center of Shandong Province, Jinan 250000, China
    3.Hydrology Bureau of the Huaihe Water Conservancy Commission, Bengbu 233001, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20231253
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表12种不同金属材料的力学参数
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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