Robust Ordered-Risk Assessment via Physics-Informed Synthetic Data Generation and TA-DE-ELM

Abstract

Ordered-risk assessment is a safety-critical learning problem in which delayed or biased risk estimation can affect prioritization and response planning. Existing expert-knowledge and data-driven approaches face a trade-off among transparent indicator design, nonlinear representation capability, and stable optimization when reliable labeled data are scarce. To address this gap, we developed a two-stage adaptive differential evolution-optimized extreme learning machine framework (TA-DE-ELM) for six-level ordered-risk assessment and evaluated it in a controlled physics-informed synthetic simulation benchmark. The benchmark encodes kinematic, capability, sensing/interference, and resilience priors as explicit scoring rules for model evaluation, rather than as an application simulator. The method combines transparent risk-logic specification, a stage-wise exploration–refinement optimizer with stagnation-triggered restart, and a validation objective that jointly considers cross-entropy, macro-F1, ordinal error, and accuracy. Under a unified finite budget, TA-DE-ELM ranked first among all tested ELM-family baselines for accuracy, macro-F1, quadratic weighted kappa, and ordinal mean absolute error, with paired tests indicating improvements over the closest competitor (p<0.013). These results show that TA-DE-ELM can recover an expert-rule-induced ordered-risk mapping more effectively than the tested baselines under controlled finite-sample conditions. Further validation with higher-fidelity simulators, externally collected datasets, and richer temporal perturbation protocols remains necessary before application-specific use.