GeoTemporal clustering for aquifer delineation: a big data approach to synchronizing and analyzing variable-length groundwater time series - Khalid ElHaj; Dalal Alshamsi (Journal of Big Data)
Abstract Groundwater is a vital global resource. However, mapping aquifers remains challenging, particularly in developing nations. This study proposes a novel methodology for aquifer delineation using time-series clustering of groundwater-level data. The modular clustering framework utilizes hierarchical agglomerative clustering and a custom hydrology-specific distance function. This accounts for the variability in the length, temporal position, and consistency of the time series, in addition to gaps in records, aligning them temporally before comparison. Advantages over traditional techniques such as dynamic time warping, and Euclidean distance are provided for analyzing real-world hydrological data. The algorithm was optimized on a synthetic Texas aquifer dataset to identify the minimum time series lengths required for accurate clustering (> 90% accuracy). Applying this to real data from the Texas Groundwater Database GWDB with over one million readings and 60,000 wells, the modeling achieved ~ 73% accuracy, delineating the nine major Texan aquifers using a filtered number of 74 representative wells. The aquifer boundaries were geographically visualized using the GeoZ library. These findings suggest the effectiveness of groundwater characterization given the limited data. The optimized algorithm could provide inexpensive mapping capabilities in developing nations, requiring only historical data from existing wells over the decades. This technique is adaptive and can be improved through ongoing monitoring. The algorithm components are modular and upgradable thus future studies should optimize and test their generalizability using additional datasets.