IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v39y2025i2d10.1007_s11269-024-03955-0.html
   My bibliography  Save this article

Study on Ensemble Calibration of Flood Forecasting Based on Response Curve of Rainfall Dynamic System and LSTM

Author

Listed:
  • Lu Tian

    (Zhengzhou University)

  • Qiying Yu

    (Zhengzhou University)

  • Zhichao Li

    (Zhengzhou University)

  • Chengshuai Liu

    (Zhengzhou University)

  • Wenzhong Li

    (Zhengzhou University)

  • Chen Shi

    (Zhengzhou University)

  • Caihong Hu

    (Zhengzhou University)

Abstract

To improve flood forecasting accuracy, the dynamic system response curve correction method was employed to invert and establish an error time series of areal rainfall in the Shouxi River Basin in Sichuan Province and the Qingyangcha Basin in Shaanxi Province. The areal rainfall in the watershed was corrected using the obtained error time series. The corrected areal rainfall was then used as input for flood forecasting using the excess storage and excess infiltration simultaneously model. Additionally, a hierarchical optimization method and LSTM error output correction method were applied to calibrate the three sources of errors. The results showed that the accuracy of flood peak discharge improved after the correction of areal rainfall. Specifically, in the validation set of the Shouxi River Basin, the absolute error of flood peak discharge decreased by 0.56% to 6.3% for 12 out of 15 flood events. The Nash–Sutcliffe Efficiency (NSE) of flood discharge increased by 0.002 to 0.015 for 13 flood events, and the time lag of two flood peaks shortened by 1 h. In the validation set of the Qingyangcha Basin, the absolute error of flood peak discharge decreased by 0.23% to 5.49% for 5 out of 6 flood events. The NSE of flood discharge increased by 0.01 to 0.071 for 5 flood events, and the time lag of two flood peaks shortened by 1 h. Overall, the results demonstrate that this method can reduce the forecast error and improve the accuracy of flood forecasting in the watershed.

Suggested Citation

  • Lu Tian & Qiying Yu & Zhichao Li & Chengshuai Liu & Wenzhong Li & Chen Shi & Caihong Hu, 2025. "Study on Ensemble Calibration of Flood Forecasting Based on Response Curve of Rainfall Dynamic System and LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(2), pages 645-660, January.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:2:d:10.1007_s11269-024-03955-0
    DOI: 10.1007/s11269-024-03955-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03955-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03955-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. P. Shirisha & K. Venkata Reddy & Deva Pratap, 2019. "Real-Time Flow Forecasting in a Watershed Using Rainfall Forecasting Model and Updating Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4799-4820, November.
    2. Bossa, A.Y. & Diekkrüger, B. & Giertz, S. & Steup, G. & Sintondji, L.O. & Agbossou, E.K. & Hiepe, C., 2012. "Modeling the effects of crop patterns and management scenarios on N and P loads to surface water and groundwater in a semi-humid catchment (West Africa)," Agricultural Water Management, Elsevier, vol. 115(C), pages 20-37.
    3. Junhao Wu & Zhaocai Wang & Yuan Hu & Sen Tao & Jinghan Dong, 2023. "Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 937-953, January.
    4. Yichao Xu & Zhiqiang Jiang & Yi Liu & Li Zhang & Jiahao Yang & Hairun Shu, 2023. "An Adaptive Ensemble Framework for Flood Forecasting and Its Application in a Small Watershed Using Distinct Rainfall Interpolation Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 2195-2219, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Youyi Zhao & Shangxue Luo & Jiafang Cai & Zhao Li & Meiling Zhang, 2024. "Monthly Precipitation Prediction Based on the CEEMDAN-BMA Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(14), pages 5661-5681, November.
    2. Danvi, Alexandre & Giertz, Simone & Zwart, Sander J. & Diekkrüger, Bernd, 2017. "Comparing water quantity and quality in three inland valley watersheds with different levels of agricultural development in central Benin," Agricultural Water Management, Elsevier, vol. 192(C), pages 257-270.
    3. Saeed Azimi & Mehdi Azhdary Moghaddam, 2020. "Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1369-1405, March.
    4. Nie Zhou & Jingming Hou & Hua Chen & Guangzhao Chen & Bingyi Liu, 2024. "A Rapid Forecast Method for the Process of Flash Flood Based on Hydrodynamic Model and KNN Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 1903-1919, April.
    5. Mojtaba Poursaeid & Amir Hossein Poursaeed & Saeid Shabanlou, 2025. "Water Resources Quality Indicators Monitoring by Nonlinear Programming and Simulated Annealing Optimization with Ensemble Learning Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 1073-1087, February.
    6. Jun Li & Songbai Song, 2023. "Urban Water Consumption Prediction Based on CPMBNIP," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(13), pages 5189-5213, October.
    7. Song-Yue Yang & You-Da Jhong & Bing-Chen Jhong & Yun-Yang Lin, 2024. "Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(4), pages 1359-1380, March.
    8. Jinghan Dong & Zhaocai Wang & Junhao Wu & Xuefei Cui & Renlin Pei, 2024. "A Novel Runoff Prediction Model Based on Support Vector Machine and Gate Recurrent unit with Secondary Mode Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(5), pages 1655-1674, March.
    9. Xin Yang & Jianzhong Zhou & Qianyi Zhang & Zhanxin Xu & Jianyun Zhang, 2024. "Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 1987-2013, April.
    10. Yichao Xu & Xinying Wang & Zhiqiang Jiang & Yi Liu & Li Zhang & Yukun Li, 2023. "An Improved Fineness Flood Risk Analysis Method Based on Digital Terrain Acquisition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3973-3998, August.
    11. Chenchen Zhao & Chengshuai Liu & Wenzhong Li & Yehai Tang & Fan Yang & Yingying Xu & Liyu Quan & Caihong Hu, 2023. "Simulation of Urban Flood Process Based on a Hybrid LSTM-SWMM Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(13), pages 5171-5187, October.
    12. Qiong Wu & Zhiqiang Jiang & Zongye Chang & Suiling Wang, 2025. "Study on Flood Control Operation of Parallel Reservoir Groups Considering the Difference of Solution Order," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(1), pages 109-125, January.
    13. Chao Wang & Zhiqiang Jiang & Pengfei Wang & Yichao Xu, 2024. "A Fast Local Search Strategy Based on the Principle of Optimality for the Long-Term Scheduling of Large Cascade Hydropower Stations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 137-152, January.
    14. Xinyu Chang & Jun Guo & Hui Qin & Jingwei Huang & Xinying Wang & Pingan Ren, 2024. "Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3953-3972, August.
    15. Wu, Junhao & Dong, Jinghan & Wang, Zhaocai & Hu, Yuan & Dou, Wanting, 2023. "A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast," Resources Policy, Elsevier, vol. 83(C).
    16. Hongyang Li & Mingxin Zhu & Fangxin Li & Martin Skitmore, 2024. "Solving flood problems with deep learning technology: Research status, strategies, and future directions," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(6), pages 7011-7035, December.
    17. Zhang, J.L. & Li, Y.P. & Wang, C.X. & Huang, G.H., 2015. "An inexact simulation-based stochastic optimization method for identifying effluent trading strategies of agricultural nonpoint sources," Agricultural Water Management, Elsevier, vol. 152(C), pages 72-90.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:39:y:2025:i:2:d:10.1007_s11269-024-03955-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.