Scholarlink

<p>To improve flood forecasting accuracy, this study proposes a hybrid model that combines a physically based rainfall-runoff model with AI-based residual prediction results. The storage function model was used to simulate runoff, while AI models (random forests, support vector regression, long short term memory, and gated recurrent unit) were used to predict the residuals. The hybrid model calculates corrected runoff by combining simulated runoff from the storage function model with AI-predicted residuals. Compared with the standalone storage function model and AI models for runoff prediction, the hybrid model demonstrated effectiveness in predicting both peak discharge and the timing of peak discharge. The proposed hybrid model can improve flood forecasting reliability and offers a valuable tool for early warning and disaster management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A residual correction approach for improving rainfall-runoff model performance in flood early warning systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月17日

查看全文
  • Haneul Lee,
  • Seungmin Lee,
  • Hoyong Lee,
  • Narae Kang,
  • Soojun Kim

摘要

To improve flood forecasting accuracy, this study proposes a hybrid model that combines a physically based rainfall-runoff model with AI-based residual prediction results. The storage function model was used to simulate runoff, while AI models (random forests, support vector regression, long short term memory, and gated recurrent unit) were used to predict the residuals. The hybrid model calculates corrected runoff by combining simulated runoff from the storage function model with AI-predicted residuals. Compared with the standalone storage function model and AI models for runoff prediction, the hybrid model demonstrated effectiveness in predicting both peak discharge and the timing of peak discharge. The proposed hybrid model can improve flood forecasting reliability and offers a valuable tool for early warning and disaster management.