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<p>Floods are severe and destructive natural disasters, and simulating and predicting flood inundation areas are among the core research topics in the field of disaster prevention and mitigation. However, the scarcity of hydrological data often limits the accuracy of flood inundation simulations. To address this issue, this study integrates multidisciplinary technologies, including Geographic Information System (GIS), hydrology, and hydraulics, and adopts the seed propagation algorithm for flood inundation simulation. A dual-case study approach was adopted, with Daoshi Town selected as the study area to demonstrate flood inundation simulations and frequency analysis under multiple return periods. The Shuiyang River and Dongjin River basins in Ningguo City were then chosen as the validation area, where the proposed method was benchmarked against mainstream models. The results show that the maximum peak water level in Daoshi Town can reach 24.75&#xa0;m during extreme flood events. The seed propagation algorithm outperforms the HEC-RAS 1D steady-flow and 2D unsteady-flow models in terms of overall spatial accuracy, achieving an F1 score of 0.76. Furthermore, the method demonstrates strong adaptability to data-scarce environments by deriving key hydrological parameters within a GIS framework and enables rapid multi-scenario flood simulation with high computational efficiency. These findings highlight the potential of the proposed approach as a practical tool for flood inundation assessment and risk analysis in small and medium-sized river basins lacking detailed hydrological data.</p>

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Flooding algorithm combining hydrology and dynamic seed growth

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Wenjie Pei,
  • Yonggang Chen,
  • Zhenkun Shi,
  • Lifeng Du

摘要

Floods are severe and destructive natural disasters, and simulating and predicting flood inundation areas are among the core research topics in the field of disaster prevention and mitigation. However, the scarcity of hydrological data often limits the accuracy of flood inundation simulations. To address this issue, this study integrates multidisciplinary technologies, including Geographic Information System (GIS), hydrology, and hydraulics, and adopts the seed propagation algorithm for flood inundation simulation. A dual-case study approach was adopted, with Daoshi Town selected as the study area to demonstrate flood inundation simulations and frequency analysis under multiple return periods. The Shuiyang River and Dongjin River basins in Ningguo City were then chosen as the validation area, where the proposed method was benchmarked against mainstream models. The results show that the maximum peak water level in Daoshi Town can reach 24.75 m during extreme flood events. The seed propagation algorithm outperforms the HEC-RAS 1D steady-flow and 2D unsteady-flow models in terms of overall spatial accuracy, achieving an F1 score of 0.76. Furthermore, the method demonstrates strong adaptability to data-scarce environments by deriving key hydrological parameters within a GIS framework and enables rapid multi-scenario flood simulation with high computational efficiency. These findings highlight the potential of the proposed approach as a practical tool for flood inundation assessment and risk analysis in small and medium-sized river basins lacking detailed hydrological data.