Qiang Ni

Sparse Incident-Cluster Learning for 12-hour Port Flood Pre-warning in Digital-Twin Analytics

This work forms 12-hour port flood pre-warning as an incident-cluster learning problem and evaluates a digital-twin analytics module using eight-point water-level histories, prediction-time contextual covariates, and interpretable short-window dynamics to create an offline-evaluated analytics and validation module desi...