Develops a decision-making framework to enhance flood warning accuracy in urban areas, suggesting significant improvements in detection and reduced underwarning rates.
Based on multisource urban flood data from Yangpu District during 2025, this study develops a hierarchical sequential decision-making framework that decouples risk detection and severity assessment through a dual-output long short-term memory (LSTM) architecture. Compared to conventional multiclass LSTMs, the framework achieves superior overall accuracy (99.77% versus 99.51%) and F1-score (0.8818 versus 0.4503), with a significantly lower false alarm rate (0.02% versus 0.06%). The framework demonstrates strong warning detection capability (62.92% exact level matching) and perfect performance on high-level warnings (Levels II and III). Compared to 79.78%, the underwarning rate of 37.08% indicates the framework’s effectiveness in alleviating the underwarning issue.
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Gu et al. (2026) studied this question.
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