# Groundwater Prediction Using Machine-Learning Tools
## 2 Background on Groundwater Prediction  
With the increase in population size coupled with urban expansion, water demand has dramatically increased, which has led to the over-exploitation of groundwater in many countries around the world [7; 8]. This highlights the importance of groundwater forecasting. Accurate prediction of groundwater can help government officials determine the best approach to manage groundwater effectively [9]. The main tools for groundwater prediction are based on physical models and data-driven models [10]. Physical models require a large amount of detailed hydrological data, which suffers from a lack of accuracy during its collection and pre-processing [9]. Therefore, data-driven models tend to be more appealing, since they require less data and are more reliable [3; 11]. Statistical models like multivariate linear regression (MLR), and various time series models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA), have been used to investigate patterns between the input and the output of groundwater data to make future predictions. The following studies have investigated: MLR for groundwater prediction [12; 13; 14]; and time series models for groundwater prediction [12; 15; 16; 17]. Both techniques are considered to be linear fitting models [11]. Time series models have the advantage of accounting for the correlations that arise between data points [18]. In general, however, linear fitting is not ideal in describing the nonlinear behavior of groundwater. Hence, recent research has made use of MLR models more for comparative purposes [11]. In addition to these techniques, a range of machine-learning techniques have been applied to the problem, including MLP in [12; 19; 20; 21; 22; 23], SVR in [19; 24] and recently RFs in [25; 26]. The use of XGB is rare in the scheme of groundwater prediction, and is found in only a few studies such as [27; 28]. The above studies can be broadly divided into those that predict the groundwater level (GWL) and those that estimate the terrestrial water storage (\(\Delta\)TWS). GWL provides an idea of the groundwaterlevel, whereas \(\Delta\)TWS provides an idea about the volume of the groundwater. The GRACE database gives geographical \(\Delta\)TWS levels monthly [6]. The significance of GRACE in hydrology is that it can provide an understanding of groundwater storage conditions and trends [29]. GRACE has been used as a predictor to help in the estimation of the GWL in [29; 30]. In the literature, there are two main approaches to the problem of sequential image prediction. The first type involves taking a sequence of images as an input to predict future frames using deep learning techniques such as Convolutional Long-Short-Term-Memory (ConvLSTM). Usually, the images used for this type of prediction are separated by relatively short time intervals e.g., 6-10 min [31; 32; 33; 34; 35; 36; 37]. This approach normally does not involve any feature selection approach, since deep learning related techniques are known for their feature selection and reduction properties. However there are several concerns when using deep learning models: they depend heavily on a large quantity of high quality data to produce an effective model; they are very costly to train and use, in terms of time and resources; deep learning models are often viewed as black boxes [38] which means that it is very difficult to unpack and understand the automated feature selection process that eventually takes place and the predictions that arise from any given deep-learning-based model. This leads us to the second approach in which machine-learning techniques can be used for single-output regressing problems. For GRACE \(\Delta\)TWS image reconstruction, the authors in [27] used both XGB and RFs to acquire the importance of 20 features. It was shown that the precipitation of the two months prior to prediction is the most important variable for estimating the TWS dynamics.