Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables

Groundwater is crucial for Africa’s potable water supply, agriculture, and economic development. However, the continent faces challenges with groundwater scarcity due to factors like population growth, climate change, and over-exploitation. Over the past ten years, machine learning has been increasingly and successfully used in groundwater availability studies across the world. This review paper explores the application of machine learning techniques in groundwater availability studies including groundwater level prediction and groundwater potential mapping studies by focusing on some of the studies conducted in Africa. The methodology involved downloading relevant papers, identifying and categorizing the machine learning algorithms employed, and quantifying their use. Geological and climatic variables were also identified, analyzed, and categorized to measure their usage frequency. The different algorithms and input variables extracted from each paper are graphically represented in this document highlighting the most employed ones. The findings suggest that more research needs to be conducted on the use of machine learning algorithms on this topic in Africa. In the reviewed papers Fuzzy-based algorithms are commonly used. The groundwater level prediction studies primarily focus on input variables related to hydrology/hydrogeology, while for potential mapping, geological aspects are the most investigated variables. In terms of climate, precipitation receives the most attention in the reviewed studies. The study highlights the potential of machine learning in improving water resource management and decision-making in the region.