cs.LG提交于 2026-10-31已译

地下水水位异常预测的选择性迁移学习：精度与风险的权衡

Selective Transfer Learning for Groundwater Level Anomaly Forecasting: Accuracy-Risk Trade-offs

Yaxuan Wu · Ali Mohammed Mansoor Alsahag · Seyed Sahand Mohammadi Ziabari

中文摘要

研究荷兰多井地下水异常预测中的上下文感知选择性迁移学习。源域预训练可提升平均精度，但在相当比例的非均质井上会降低性能。上下文感知选择性学习（CASL）通过校准训练的、受约束的上下文混合，融合冻结的无迁移与固定迁移父模型。全局凸混合使用相同父模型，每个种子拟合一个校准权重。修正后的任务要求连续九个月观测输入和精确的三个日历月目标，不做插值。评估覆盖 457 口井、13,718 个测试窗口，采用五个预定义种子，四格 maxsim0.60 源池仅依据 Always-TL 验证 RMSE 冻结。固定迁移损害 18.38-51.86% 的井。CASL 的 RMSE 比无迁移低 0.424-1.084%，部分挽救 97.53-99.50%、完全挽救 23.46-43.78% 的受固定迁移损害井。部分挽救可由凸混合结构性产生，不能证明上下文优越性。全局凸混合在四个种子中保护更多井，CASL 在四个种子中汇总 RMSE 略低。两者均不占优：结果支持固定划分上的精度-风险权衡，而非滚动部署有效性。

关键要点

01源域预训练提升平均精度，但固定迁移损害 18.38-51.86% 的井，暴露异质性风险。
02CASL 用校准约束的上下文混合融合冻结父模型，RMSE 比无迁移低 0.424-1.084%。
03CASL 部分挽救 97.53-99.50% 受损害井，但部分挽救可由凸混合结构性产生。
04全局凸混合与 CASL 互不占优：前者保护更多井，后者汇总 RMSE 略低。
05结论仅支持固定划分上的精度-风险权衡，未验证滚动部署有效性。

解读

尚无解读。

原始英文摘要

arXiv:2610.37733v1 Announce Type: cross Abstract: This work studies context-aware selective transfer learning for multi-well groundwater anomaly forecasting in the Netherlands. Source-domain pretraining can raise average accuracy but is not uniformly reliable: it degrades performance on a substantial fraction of heterogeneous wells. Context-Aware Selective Learning (CASL) uses calibration-trained, constrained contextual blending of frozen no-transfer and fixed-transfer parents. Global Convex Blend uses the same parents with one calibration-fitted weight per seed. The corrected task requires nine consecutive observed monthly inputs and an exact three-calendar-month target, without interpolation. Evaluation covers 457 wells and 13,718 test windows under five predefined seeds, with the four-cell maxsim0.60 source pool frozen using Always-TL validation RMSE alone. Fixed transfer harms 18.38-51.86% of wells. CASL has 0.424-1.084% lower RMSE than No TL, partially rescuing 97.53-99.50% and fully rescuing 23.46-43.78% of fixed-harmed wells. Partial rescue can arise structurally from convex blending and does not establish contextual superiority. Global Convex Blend protects more wells in four seeds, while CASL has slightly lower pooled RMSE in four. Neither dominates: the results support an accuracy-risk trade-off on the fixed split, not rolling deployment validity.