arXiv cs.AI· Saleh Sakib Ahmed, Rashed Uz Zzaman, Saifur Rahman Jony, Faizur Rahman Himel, Afroza Sharmin, A. H. M. Khalequr Rahman, M. Sohel Rahman, Sara Nowreen·· 4 小时前AI 评分35
孟加拉国地下水研究：可跨时空泛化的模型揭示水位下降与补给同步衰退
Unraveling Groundwater Stress: A Model Generalizable Across Space and Time Reveals Co-Declining Water Tables and Recharge Across Bangladesh
AI 导读
研究者提出 Teacher-Student 框架，利用稀疏实测数据（最低、最高水位 R²=0.86/0.96）训练 Teacher，再由 Student 将 25-km GLDAS 地下水储量转为 2-km 高分辨率水位图，2003-2022 期间 R²=0.93，并能在未见年份（LOYO R²=0.93）和地区（LODO R²=0.70）泛化。
正文 · 原文
Abstract:Is Bangladesh replenishing the groundwater it depends on? Answering this question remains difficult in Bangladesh and worldwide because sparse in-situ measurements limit observational capabilities, while existing machine-learning approaches struggle to generalize across time and space. To address this bottleneck, we develop a Teacher-Student framework in which a Teacher accurately learns from sparse annual in-situ minimum (R2 = 0.86) and maximum (R2 = 0.96) depth observations to generate dense spatial patterns, and a Student transfers these patterns from time-varying 25-km GLDAS groundwater storage to high-resolution (2-km) depth maps using location-specific features. The Student achieves R2 = 0.93 over 2003-2022 and generalizes across unseen years (Leave-One-Year-Out R2 = 0.93) and locations (Leave-One-District-Out R2 = 0.70), enabling reconstruction without contemporary in-situ measurements. The reconstruction reveals widespread water-table deepening and a national decline in groundwater recharge of 1.10 mm/yr (p < 0.001). Autocorrelation-corrected analysis identifies statistically significant recharge declines across 55.6% of Bangladesh, compared with significant increases across only 4.9%. This imbalance suggests that pumping-induced recharge enhancement, as proposed by the Bengal Water Machine hypothesis, remains localized rather than widespread across Bangladesh. Together, these findings reveal widespread weakening of groundwater replenishment and provide a scalable framework for monitoring groundwater dynamics in data-sparse aquifers globally.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2503.22771 [cs.LG] |
| (or arXiv:2503.22771v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2503.22771 arXiv-issued DOI via DataCite |
Submission history
From: M. Sohel Rahman [view email]
[v1]
Fri, 28 Mar 2025 04:56:01 UTC (72,695 KB)
[v2]
Wed, 4 Nov 2026 03:08:02 UTC (66,494 KB)
来源：arXiv cs.AI · arxiv.org