mlsanityData to 14 Oct 2026

Uncovering Insights of Compound Flooding with Data-Driven AI

arXiv 2506.04281 ↗First posted 4 Jun 2025cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approaches, whether physics-based or data-driven, often emphasize temporal patterns while underexploring how multiple interacting factors jointly shape flood dynamics. To address this problem, we conduct a large-scale data-driven analysis of compound flooding in South Florida, a typical area for compound flooding, by integrating tidal conditions, rainfall, groundwater stage, and human water management activities. Our analysis reveals three key findings: (i) models that capture temporal dynamics alone fail to represent multi-factor interactions during compound events; (ii) subsurface saturation, as reflected by groundwater levels, emerges as a dominant predictor of flood severity, often outweighing immediate rainfall intensity in this porous coastal region; and (iii) the spatial state of surrounding monitoring stations within a finite effective radius provides critical causal context for flooding, while extending temporal history yields diminishing returns during extreme events. These findings suggest that compound flooding is governed more by spatially coupled system states than by long-term temporal dependencies, challenging rain-centric and sequence-dominated forecasting paradigms. By framing data-driven models as tools for scientific inquiry rather than prediction alone, this study offers new insights into the mechanisms of compound flooding and informs the design of more physically grounded early-warning systems for coastal environments. Our dataset and code are publicly available at https://github.com/AslanDing/SFBench.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 30 of its 89 references that are in the map. First posted on arXiv 4 Jun 2025.

Builds on

30 of 89 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problemscited by 0
  2. 2026Towards A Unified Information Bottleneck Framework for Time Series Explanationscited by 0