The new paradigm of hydrogeology: A critical review of remote sensing and AI approaches for groundwater assessment and monitoring in Sub-Saharan Africa

Abstract

Faced with growing water scarcity, the delineation of groundwater potential zones (GWPZ) using remote sensing and machine learning techniques has become an indispensable tool. This article critically reviews the evolution and application of these techniques between 2000 and 2025. We synthesize prevailing approaches, document progress, address critical challenges, and identify urgent research opportunities to guide future sustainable groundwater exploration and management. Our review demonstrates that the advent and continuous advancements of remote sensing, providing critical long-term data, have significantly propelled the field of GWPZ. The availability of various multi-spectral sensors and sophisticated models has been instrumental in this progress. Additionally, emerging trends such as machine learning techniques (e.g., Random Forest and Support Vector Machine) and cloud computing platforms enabled effective multi-source data fusion, enhancing predictive accuracy and overcoming limitations of individual sensors. However, significant challenges persist. The scarcity of ground-based data impedes robust model validation, while a lack of high-resolution spatial and temporal data hinders precise and small-scale delineation. This is particularly acute in Sub-Saharan Africa, which remains critically understudied. To address these gaps, we recommend that future research work focus on using spatial cross-validation to achieve accurate results, assess data-scarcity solutions including data augmentation, use advanced approaches such as explainable artificial intelligence, hybrid and ensemble machine learning models to improve delineation precision and integrate these approaches into broader water resource resilience frameworks.