发表机构
Lancaster University; Middlesex University（兰卡斯特大学; 密德萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对港口洪水预警中事件间时间依赖导致评估高估的问题，提出事件簇学习框架，结合稀疏特征选择与top-k截断，在利物浦案例中实现F2=0.696，优于基线，并集成到数字孪生模块。
AI 中文摘要
港口洪水数字孪生需要能够在中断发生前向操作员发出警告的分析方法，但官方警告事件往往很少，且相邻观测在时间上具有依赖性。因此，行级分类可能通过将来自同一事件的窗口同时置于模型开发和评估数据中而高估性能。我们将12小时港口洪水预警表述为一个事件簇学习问题，并使用八点水位历史、预测时上下文协变量和可解释的短窗口动态来评估数字孪生分析模块。该协议结合了折特定稀疏特征选择、警告簇分组、负标签对照、100次重复随机top-k对照和警报事件评估。利物浦是主要的四簇案例研究，使用协调的亨伯/赫尔代理和威塞克斯南部数据进行协议迁移检查。在利物浦各折中，top-10 ElasticNet模型达到平均F2 = 0.696，相比之下，无top-k截断时为0.633，全特征加权XGBoost为0.681。它是最强的ElasticNet变体，仅使用十个预测因子即可与非线性参考保持竞争力，并超过重复级别95百分位的广泛和同族随机子集。上下文协变量提供了强大的预测时锚点，辅以物理上可解释的局部动态。历史回放将风险评分转换为警报事件，并衡量警报持续时间和误报事件负担。结果是一个离线评估的分析和验证模块，旨在集成到港口数字孪生中。
英文摘要
Port flood digital twins require analytics that warn operators before disruption, but official warning incidents are often few and adjacent observations are temporally dependent. Row-level classification can therefore overstate performance by placing windows from the same event in both model-development and evaluation data. We formulate 12-hour port flood pre-warning as an incident-cluster learning problem and evaluate a digital-twin analytics module using eight-point water-level histories, prediction-time contextual covariates, and interpretable short-window dynamics. The protocol combines fold-specific sparse feature selection, warning-cluster grouping, negative-label controls, 100-repeat random top-k controls, and alert-episode evaluation. Liverpool is the primary four-cluster case study, with harmonised Humber/Hull-proxy and Wessex South data used for protocol-transfer checks. Across the Liverpool folds, the top-10 ElasticNet model achieves mean F2 = 0.696, compared with 0.633 without top-k truncation and 0.681 for full-feature weighted XGBoost. It is the strongest ElasticNet variant, remains competitive with the nonlinear reference using only ten predictors, and exceeds the repeat-level 95th percentile of broad and same-family random subsets. Contextual covariates provide a strong prediction-time anchor, complemented by physically interpretable local dynamics. Historical replay converts risk scores into alert episodes and measures alert duration and false-episode burden. The result is an offline-evaluated analytics and validation module designed for integration into a port digital twin.
