Aquifer test analysis is complex, and in many regards, the interpretation resembles an art more than a science. Under the best circumstances, aquifer test analysis is still plagued by ambiguity and uncertainty, compounded by the general lack of information on the subsurface. An approach which has seen widespread adoption in other fields that need to classify time series data is machine learning. A Python script that generates numerical groundwater flow models by interfacing directly with the modelling software produces training data for deep learning.

Machine learning techniques are gaining recognition as tools to underpin water resources management. Applications range widely, from groundwater potential mapping to the calibration of groundwater models. This research applies machine learning techniques to map and predict nitrate contamination across a large multilayer aquifer in central Spain. The overall intent is to use the results to improve the groundwater monitoring network. Twenty supervised classifiers of different families were trained and tested on a dataset of fifteen explanatory variables and approximately two thousand points.

 Predicting and quantifying the hydrogeological interference of big underground works is a complex effort. This is due to the considerable uncertainty in estimating the key geomechanical and hydrogeological parameters affecting the area of potential interference of the projects. Moreover, the pattern of involved groundwater flow systems is hardly identified, either in natural or disturbed conditions. Base tunnels through mountain ridges are particularly complex in their interactions with groundwater.

The beneficial groundwater use in the Southern African Development Community (SADC) is well documented. Groundwater plays a vital role in the freshwater supply mix and, in some cases, is the only source of freshwater, especially in the arid region of SADC. However, the management of this resource is hampered by numerous challenges, such as lack of data, limited tools to leverage available data, lack of resources, institutional mismanagement, and climate change, amongst others.

The lack of reliable groundwater level monitoring data hinders the comprehensive understanding and sustainable management of our aquifers. New remotely sensed data products could present novel possibilities to fill in situ data gaps. For example, continuous monthly groundwater storage anomaly estimates at a spatial resolution of 0.25° (28 km) are made available through the Global Data Assimilation System Version 2.2 (GLDAS-2.2) data products that assimilate Gravity Recovery and Climate Experiment (GRACE) data.

Groundwater systems are complex and subject to climate change, abstraction, and land use stresses, making quantifying their impacts on aquifers difficult. Groundwater models aim to balance abstraction and aquifer sustainability by simulating the responses of an aquifer to hydrological stresses through groundwater levels. However, these models require extensive spatial data on geological and hydrological properties, which can be challenging to obtain. To address this issue, data-driven machine learning models are used to predict and optimize groundwater levels using available data.

Salinization is one of the main threats to groundwater quality worldwide, affecting water security, crop productivity and biodiversity. The Horn of Africa, including eastern Ethiopia, northeast Kenya, Eritrea, Djibouti, and Somalia, has natural characteristics favouring high groundwater salinity. However, available salinity data are widely scattered, lacking a comprehensive overview of this hazard. To fill this gap, machine learning modelling was used to spatially predict patterns of high salinity with a dataset of 6300 groundwater quality measurements and various environmental predictors.

Understanding the sensitivity of groundwater resources to surface pollution and changing climatic conditions is essential to ensure its quality and sustainable use. However, it can be difficult to predict the vulnerability of groundwater where no contamination has taken place or where data are limited. This is particularly true in the western Sahel of Africa, which has a rapidly growing population and increasing water demands.

The advent of the 'Big Data' age has fast tracked advances in automated data analytics, with significant breakthroughs in the application of artificial intelligence (AI). Machine learning (ML), a branch of AI, brings together statistics and computer science, enabling computers to learn how to complete given tasks without the need for explicit programming. ML algorithms learn to recognize and describe complex patterns and relationships in data - making them useful tools for prediction and data-driven discovery.