地下水位异常预测的选择性迁移学习：精度与风险权衡研究
Selective Transfer Learning for Groundwater Level Anomaly Forecasting: Accuracy-Risk Trade-offs
AI 导读
一项针对荷兰多井地下水异常预测的研究提出上下文感知选择性学习（CASL），用冻结的无迁移与固定迁移双母模型的凸组合降低固定迁移的风险。基于 457 口井、13,718 个测试窗口的评估显示，固定迁移会损害 18.38%-51.86% 的井，CASL 比 No TL 的 RMSE 低 0.424%-1.084%，部分挽回 97.53%-99.50% 受损井。
正文 · 原文
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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37733 [cs.LG] |
| (or arXiv:2610.37733v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37733 arXiv-issued DOI via DataCite |
Submission history
From: Ali Alsahag [view email]
[v1]
Tue, 27 Oct 2026 13:15:35 UTC (301 KB)
来源：arXiv cs.AI · arxiv.org