Leveraging machine learning to extract insights and spatial patterns from hydrogeochemical datasets for major groundwater regions in the UAE

Abstract

Groundwater samples were collected from 45 wells across three regions in the United Arab Emirates (UAE)—Jabel Hafeet, Fujairah, and Ras Al Khaimah; their major-ion chemistry and radon activity were analyzed to characterize hydrogeochemical facies. Machine learning (ML) techniques were employed to impute the missing chloride and sulfate concentrations for any missing samples. In this regard, the best models accuracy wise were optimized Random Forest and Extra-trees. The resultant complete dataset was subjected to unsupervised K-means clustering. Mapping the clusters using GeoZ library revealed distinct spatial patterns related to different geological settings. Most Fujairah and Ras Al Khaimah samples clustered together, indicating aquifer similarity, while the Jabel Hafeet samples clustered separately. Several Jabel Hafeet surface water samples were clear outliers. Within the clusters, radon exhibited variation related to groundwater source and could be a useful environmental tracer. The study demonstrates that machine learning could be used to extract meaningful information from incomplete geoscience data. Major findings were the hydrogeochemical similarities between the Fujairah and Ras Al Khaimah aquifers and their differences with the Hafeet aquifer, identification of the Jabel Hafeet surface water samples, and utility of radon in environmental tracing. This research provides valuable insights into major UAE aquifers and the ability of artificial intelligence to boost the value of imperfect datasets.

Highlights

Machine learning can predicts missing hydrogeochemical data, enhancing groundwater insights.

Radon is a valuable tracer for tracking groundwater sources in arid regions.

Fujairah and Ras Al Khaimah aquifers exhibit similar water chemistry profiles.