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<p>Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO–TCN–Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang by integrating the particle swarm optimisation (PSO) algorithm, temporal convolutional network (TCN), and Bootstrap probability sampling method. Evaluated on 50 historical flood events from 1960 to 2014 using observed rainfall-runoff data, the model showed, under the same lead time conditions, a higher Nash efficiency coefficient, along with lower root mean square and relative peak errors in flood forecasting. These results highlight the PSO–TCN–Bootstrap model’s superior applicability and robustness for the Tailan River Basin. However, when the lead time exceeds 5 h, the model’s relative peak error remains above 20%. Future work will focus on integrating flood generation mechanisms and enhancing machine learning models’ generalisability in flood forecasting. These findings provide a scientific foundation for flood management strategies in the Tailan River Basin.</p>

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Research on a hybrid model for flood probability prediction based on time convolutional network and particle swarm optimization algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月26日

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  • Qiying Yu,
  • Chengshuai Liu,
  • Runxi Li,
  • Zhenlin Lu,
  • Yungang Bai,
  • Wenzhong Li,
  • Lu Tian,
  • Chen Shi,
  • Yingying Xu,
  • Biao Cao,
  • Jianghui Zhang,
  • Caihong Hu

摘要

Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO–TCN–Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang by integrating the particle swarm optimisation (PSO) algorithm, temporal convolutional network (TCN), and Bootstrap probability sampling method. Evaluated on 50 historical flood events from 1960 to 2014 using observed rainfall-runoff data, the model showed, under the same lead time conditions, a higher Nash efficiency coefficient, along with lower root mean square and relative peak errors in flood forecasting. These results highlight the PSO–TCN–Bootstrap model’s superior applicability and robustness for the Tailan River Basin. However, when the lead time exceeds 5 h, the model’s relative peak error remains above 20%. Future work will focus on integrating flood generation mechanisms and enhancing machine learning models’ generalisability in flood forecasting. These findings provide a scientific foundation for flood management strategies in the Tailan River Basin.