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Coupling Time and Non-Time Series Models to Simulate the Flood Depth at Urban Flooded Area

Author

Listed:
  • Hongfa Wang

    (Zhengzhou University)

  • Xinjian Guan

    (Zhengzhou University)

  • Yu Meng

    (Zhengzhou University)

  • Zening Wu

    (Zhengzhou University)

  • Kun Wang

    (China Institute of Water Resources and Hydropower Research)

  • Huiliang Wang

    (Zhengzhou University)

Abstract

The flood produced by short duration heavy rainfall events in cities will still exist after raining and continues to cause harm and impact. To accurately predict the depth and duration of the flood, a coupled model of the extreme gradient boosting and long short-term memory algorithms was proposed. A practical application of three representative flooded points in the Zhengzhou city, China, the results showed the coupled model could fit and forecast the flood. The average of Mean relative error, Nash–Sutcliffe efficiency coefficient and Qualified rate of validation data were 9.13%, 0.96 and 90.3% respectively, which verified the superiority of the method in the flood prediction. And the flood processes at the flooded points caused by design rainfall under different return periods were predicted by the coupled model. The growth rates of the flood duration and peak flood depth were all the highest during the return periods 1a-2a. This study proves that the coupled model has great potential in predictions of flood and could provide scientific basis guidance for disaster reduction.

Suggested Citation

  • Hongfa Wang & Xinjian Guan & Yu Meng & Zening Wu & Kun Wang & Huiliang Wang, 2023. "Coupling Time and Non-Time Series Models to Simulate the Flood Depth at Urban Flooded Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1275-1295, February.
  • Handle: RePEc:spr:waterr:v:37:y:2023:i:3:d:10.1007_s11269-023-03430-2
    DOI: 10.1007/s11269-023-03430-2
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    References listed on IDEAS

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    1. Shuang Yao & Nengcheng Chen & Wenying Du & Chao Wang & Cuizhen Chen, 2021. "A Cellular Automata Based Rainfall-Runoff Model for Urban Inundation Analysis Under Different Land Uses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1991-2006, April.
    2. Wenchao Qi & Chao Ma & Hongshi Xu & Zifan Chen & Kai Zhao & Hao Han, 2021. "Low Impact Development Measures Spatial Arrangement for Urban Flood Mitigation: An Exploratory Optimal Framework based on Source Tracking," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3755-3770, September.
    3. Zhang, Yuliang & Wu, Zhiyong & Singh, Vijay P. & He, Hai & He, Jian & Yin, Hao & Zhang, Yaxin, 2021. "Coupled hydrology-crop growth model incorporating an improved evapotranspiration module," Agricultural Water Management, Elsevier, vol. 246(C).
    4. Brenden Jongman, 2018. "Effective adaptation to rising flood risk," Nature Communications, Nature, vol. 9(1), pages 1-3, December.
    5. Lin She & Xue-yi You, 2019. "A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3143-3153, July.
    6. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    7. Bing-Chen Jhong & Jhih-Huang Wang & Gwo-Fong Lin, 2016. "Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4247-4271, September.
    8. Han, Tian & Peng, Qinke & Zhu, Zhibo & Shen, Yiqing & Huang, Huijun & Abid, Nahiyoon Nabeel, 2020. "A pattern representation of stock time series based on DTW," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    9. Tao Cheng & Zongxue Xu & Siyang Hong & Sulin Song, 2017. "Flood Risk Zoning by Using 2D Hydrodynamic Modeling: A Case Study in Jinan City," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-8, October.
    10. Bao-Jian Li & Guo-Liang Sun & Yan Liu & Wen-Chuan Wang & Xu-Dong Huang, 2022. "Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2095-2115, April.
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