# Groundwater Prediction Using Machine-Learning Tools
## 1 Introduction  
In many countries, groundwater is one of the key natural resources that supplies a large portion of the water used by a nation. Besides its use in households and businesses, some other groundwater consumers include: (i) rural households and public water supplies that depend on wells and groundwater; (ii) farmers who use groundwater to irrigate crops and water their animals; and (iii) commercial businesses and industries that depend on groundwater for their processes and operations. Furthermore, the importance of groundwater can be revealed in its usage in supplying springs, water in ponds, marshlands, swamps, streams, rivers and bays. However, despite its unequivocal importance, groundwater levels in aquifer systems are often not constant and depend on recharge from infiltration of precipitation.  
Several major acts and regulations such as the South African national water Act [1] and the 4 th World Water Forum [2] recognize water as a basic human need, which is a major contributor to social development since it helps to alleviate poverty [1]. Hence, there is a growing interest towards the use of groundwater to help alleviate this crisis [2]. Groundwater is a vital freshwater resource which provides around 50% of the available drinking water according to UNESCO [3]. Also, sectors likeagriculture, and industry greatly depend on groundwater for their operations due to its widespread availability and the fact that it is not easy polluted [3; 4]. Therefore, in 2015 the United Nations have reaffirmed their commitment regarding the human right to safe drinking water and sanitation by identifying it as one of the 17 Sustainable Development Goals to be pursued by 2030 [5].  
Predicting groundwater availability is important to water sustainability and drought mitigation. It can provide useful insights based on real data of what happened when the flow of streams and rivers declined and/or when water supply issues developed into a drought. Machine-learning tools technologies have the potential to drive groundwater knowledge discovery and management by assisting in the prediction of groundwater availability. This can be done by enabling the collection of massive water datasets, storing these datasets into databases and processing these datasets to get useful insights which can be used by water resource managers to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater protection strategies; and (4) evaluate the sustainability of groundwater sources of drinking water.  
This paper uses a regression-based approach to predict full groundwater images based on sequences of monthly groundwater maps of the southern part of the African continent using the Gravity Recovery and Climate Experiment (GRACE) dataset [6]. Five machine-learning techniques are implemented on the GRACE dataset and compared to predict pixels in future frames of the dataset. These are extreme gradient boosting (XGB), multivariate linear regression (MLR), random forest (RF), multilayer perceptron (MLP) and support vector regression (SVR). The prediction is guided by: (i) performing feature selection based on the XGB feature importance bar on the previous lags (pixels); and (ii) investigating the effect of adding other features such as the temporal features, position indices, and global features obtained by Gaussian mixture models (GMMs) fitted to peak areas on each image.  
This paper is organized as follows: Section 2 provides a background on water prediction, citing relevant literature in the field; Section 3 describes the algorithms used in this work; Section 4 discusses the methodology used for ground water prediction; Section 5 provides and discusses the results obtained; and Section 6 furnishes the conclusions.