arXiv cs.AI· Yaxuan Wu, Ali Mohammed Mansoor Alsahag, Seyed Sahand Mohammadi Ziabari·· 14 小时前AI 评分18
CASL：荷兰多井地下水水位异常预测的选择性迁移学习与精度-风险权衡
Selective Transfer Learning for Groundwater Level Anomaly Forecasting: Accuracy-Risk Trade-offs
AI 导读
研究提出 Context-Aware Selective Learning（CASL），对冻结的无迁移与固定迁移父模型做校准训练的约束上下文混合，用于荷兰 457 口井、13,718 个测试窗口的地下水异常预测。
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