两阶段推荐系统预测热带气旋对港口的影响天数排序
Robust Ranking of Port Impacts from Impending Tropical Cyclones: A Two-Stage Recommendation System
用推荐系统思路预测台风会打乱哪些港口、影响几天，Wide & Deep 模型比距离基线高出 55%-85%，做物流和防灾的可以看看方法。
论文提出一个两阶段推荐框架，先用登陆位置邻近性筛选候选港口，再按预计影响天数排序。研究基于墨西哥湾 37 个港口、31 场热带气旋的 AIS 影响天数数据，比较了成对特征交互、树模型特征组合和特征交叉加 embeddings 三种表示方式。在飓风 Nicole 和 Ian 的测试中，检索阶段在登陆位置不确定的情况下仍能召回全部实际受影响港口。Wide & Deep Learning 排序模型的平均排序一致性比距离基线分别高出 85.5%（Nicole）和 55.5%（Ian），且对登陆位置误差不敏感。该排序可用于港口部门安排灾后检查优先级和分配恢复资源。
Robust Ranking of Port Impacts from Impending Tropical Cyclones: A Two-Stage Recommendation System
When a tropical cyclone (TC) threatens several ports, preparedness planning depends on anticipating which ports are likely to be affected and where disruptions may last longest. Limited historical observations at individual ports and uncertainty in landfall location make these relative impacts difficult to estimate. We use a two-stage recommendation framework that first selects candidate ports based on their proximity to the expected landfall location and then ranks them by their estimated number of impact days. Using AIS-derived impact days for 37 ports and 31 TCs in the Gulf of Mexico study region, we compare three ways of representing port--cyclone interactions: pairwise feature interactions, tree-based feature combinations, and explicit feature crosses combined with learned embeddings. In tests on Hurricanes Nicole and Ian, the retrieval stage consistently identifies all observed affected ports under landfall uncertainty. Among the ranking models, Wide & Deep Learning achieves the closest agreement with the observed port rankings. Its mean ranking agreement exceeds that of the distance-based baseline by 85.5% for Nicole and 55.5% for Ian and is less sensitive to uncertainty in the landfall location. These rankings could help port authorities prioritize post-cyclone inspections and allocate recovery resources across ports threatened by the same event.
