Title: Leveraging Physics-Informed Machine Learning to Enhance Short-Term Flood Forecasting

Abstract:

Precise flood forecasting plays a critical role in disaster risk reduction and community safeguarding. Nevertheless, machine learning approaches that rely exclusively on data often falter in environments where information is scarce and may inadvertently breach core hydrological laws. Standard Long Short-Term Memory (LSTM) networks, for instance, are prone to producing predictions that lack physical consistency, especially when extrapolating toward extreme weather scenarios. To overcome these hurdles, this study introduces a Physics-Informed Machine Learning (PIML) framework that embeds hydrological expertise directly into the LSTM model’s loss function. Central to this approach is a Trend Alignment constraint, which penalizes directional mismatches between precipitation and discharge trends. This method boosts model robustness without necessitating complex hydrodynamic equations. By encouraging the learning of physically plausible hydrograph patterns, this regularization technique ensures reliability during peak flood events, even when training data is limited.

Experimental outcomes indicate that the physics-informed model surpasses a standard LSTM baseline in data-scarce contexts. Specifically, when trained on merely 5% of the available dataset, the proposed method raised the Nash-Sutcliffe Efficiency (NSE) score from 0.20 to 0.23. Further stress tests involving simulated extreme climate conditions revealed that while the baseline model displayed unstable behavior, the physics-informed variant preserved both directional consistency and physical plausibility. While determining the exact magnitude of extreme peaks remains difficult with restricted data, the new approach significantly curbs the unphysical fluctuations typically seen in purely data-driven models. These results suggest that integrating straightforward physical constraints can markedly enhance the dependability of deep learning systems for real-time flood forecasting, providing a viable strategy for ungauged basins and shifting climate patterns.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC