This paper proposes NeuroHazard, a novel framework for real-time geohazard forecasting that integrates neural emulation with streaming sensor data through a physics-informed digital twin architecture. The approach combines a Fourier Neural Operator-based surrogate model, Ensemble Kalman Filter data assimilation, Bayesian uncertainty quantification, and an adaptive early warning system for sub-second forecasting essential for emergency response.
Key findings
Achieves 100,000x speedup over traditional numerical solvers with Fourier Neural Operator.
Ensemble Kalman Filter integrates heterogeneous sensor observations for continuous model state updates.
Bayesian neural networks provide calibrated probabilistic forecasts with explicit uncertainty decomposition.
Adaptive early warning system with dynamic threshold adjustment based on real-time risk assessment.
Limitations & open questions
The framework's performance under real-world conditions requires comprehensive validation.
The integration of neural emulation, real-time data assimilation, and adaptive early warning presents technical challenges.