Threshold Effects and Generalization Bias in AI-based Urban Pluvial Flood Prediction: Insights from a Dataset Design Perspective

Abstract. Reliable urban flood prediction hinges on how datasets are designed, yet most existing research disproportionately emphasizes network architectures over data foundations. This study systematically investigates how dataset characteristics—scale, feature composition, and rainfall-event distribution—govern predictive performance and generalization in AI-based flood modeling. A physically calibrated hydrological–hydrodynamic model was employed to generate synthetic datasets with varied temporal lengths, input feature combinations (rainfall, infiltration, drainage), and rainfall-intensity distributions. A long short-term memory (LSTM) network, chosen for its widespread adoption and proven performance in hydrology, was applied as a representative benchmark to assess accuracy, computational cost, and bias under controlled conditions. Results identify: (1) a threshold effect of dataset length (~14,400 samples), beyond which performance gains plateau; (2) rainfall-intensity distribution as the dominant driver of generalization—training solely on light or extreme events induces systematic bias, whereas mixed-intensity datasets substantially enhance robustness; (3) ancillary features (infiltration and drainage) improve stability only when data are sufficiently abundant. These findings quantify trade-offs and pinpoint actionable design levers, offering general insights into dataset design for machine learning models in flood prediction and beyond. By clarifying critical dataset requirements, this study provides transferable guidance for building efficient and balanced datasets in hydrology and broader Earth system sciences.