Predicting and Interpolating Spatiotemporal Environmental Data: A Case Study of Groundwater Storage in Bangladesh

TL;DR

This paper compares deep learning approaches for predicting and interpolating groundwater storage data in Bangladesh, revealing that spatial interpolation is more challenging than temporal prediction and highlighting the influence of geological uncertainties.

Contribution

It introduces and evaluates two deep learning strategies for spatiotemporal environmental data prediction and interpolation, providing insights into their relative effectiveness and challenges.

Findings

Spatial interpolation is more difficult than temporal prediction.

Nearest neighbors are not always the most similar in spatial data.

Geological uncertainties significantly affect point temporal behavior.

Abstract

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.