A Physics-Informed Deep Learning Framework for Enhancing Rare Low-Visibility Event Prediction

Accurate visibility prediction faces challenges from extreme data imbalance and complex spatiotemporal dependencies. This study develops an enhanced deep learning framework based on Informer architecture for short-term visibility prediction, trained on station-based observations in 2019-2024. To address extreme sample imbalance in visibility data, we have optimized data preprocessing and implemented physical constraints to the Informer architecture, specifically targeting improved prediction of low-visibility events like fog that hold significant public safety implications. First, visibility values were confined to a threshold range of 0.01 to 15 km, followed by a logarithmic-reciprocal transformation to nonlinearly expand the value interval for low-visibility conditions and inherently enhance their weighting within the model. Correspondingly, the activate function at the final output layer was also constrained to this threshold range to ensure physically realistic predictions. In addition, we propose a differentiable Threat Score-based loss function (TSLoss) that complements the mean squared error (MSE) loss, strategically weighting errors in rare low-visibility events. This approach resolves the non-differentiability of regression-to-binary conversion through sigmoid-activated thresholds. For comparative analysis, three models were trained: LSTM, standard Informer, and our modified Informer_TS. Evaluated against two baseline models, the optimized Informer_TS achieves superior performance for rare low-visibility events (≤1 km TS = 0.3, peaking at 0.55 at t+0) especially for significant reduction in false alarms. It performs especially well at coastal fog-prone sites and effectively captures nocturnal low-visibility events with better stability. Interpretability analyses highlight visibility autocorrelation, diurnal cycles, and meridional wind as key features. The algorithm demonstrates significant operational value for maritime and aviation safety through nowcasting of rapid-onset fog.