用深度学习预测与插值孟加拉地下水储量，发现空间插值比时间预测难得多
Predicting and Interpolating Spatiotemporal Environmental Data: A Case Study of Groundwater Storage in Bangladesh
- 先建模后插值的网格-点方法优于先聚合再建模的网格-网格方法
- 空间插值难度大，地质不确定性显著影响时间序列行为
- 适用于受间接因素影响的环境变量，如土壤湿度、地表温度等
地理空间观测数据通常仅限于点测量，因此时间预测与空间插值对构建连续场至关重要。本研究评估了两种深度学习策略：(1) 网格到网格方法，即使用栅格预测器建模栅格目标（建模前聚合）；(2) 网格到点方法，即使用栅格预测器建模点目标，再通过克里金插值填充整个区域（建模后聚合）。以孟加拉国地下水储量为案例，比较两种方法的效果。结果表明，空间插值远比时间预测困难，最近邻并不总是最相似的，地质不确定性强烈影响点的时间行为。这些发现推动未来基于时间序列动态聚类位置的先进插值方法研究。研究结论在地下水储量上验证，可推广至其他受间接观测因素影响的环境变量。代码已公开于 https://github.com/pazolka/interpolation-prediction-gwsa。
原文摘要 · Abstract (English)
Geospatial observational datasets are often limited to point measurements, making temporal prediction and spatial interpolation essential for constructing continuous fields. This study evaluates two deep learning strategies for addressing this challenge: (1) a grid-to-grid approach, where gridded predictors are used to model rasterised targets (aggregation before modelling), and (2) a grid-to-point approach, where gridded predictors model point targets, followed by kriging interpolation to fill the domain (aggregation after modelling). Using groundwater storage data from Bangladesh as a case study, we compare the effcacy of these approaches. Our findings indicate that spatial interpolation is substantially more difficult than temporal prediction. In particular, nearest neighbours are not always the most similar, and uncertainties in geology strongly influence point temporal behaviour. These insights motivate future work on advanced interpolation methods informed by clustering locations based on time series dynamics. Demonstrated on groundwater storage, the conclusions are applicable to other environmental variables governed by indirectly observable factors. Code is available at https://github.com/pazolka/interpolation-prediction-gwsa.
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