## 2 Background Literature
### Machine Learning for Groundwater Level Change Prediction  
The resurgence of attention in machine learning is due to the growing volumes and types of available data, less expensive computational handling, and all-the-more ground-breaking and modest data storage [31]. In recent years, academics have created a number of machine learning algorithms for forecasting stock trends [32] and groundwater level prediction [33], for example. Today, even on a massive scale, it is possible to quickly and organically develop models that can dissect larger, more mind-boggling data and deliver faster, more exact results. The ability of machine learning, which has the capability to learn complex correlations between the important hydrogeological factors (HGFs) and the groundwater level (GWL), is essential [34]. There are several machine learning algorithms available, including classification and regression models. Artificial intelligence (AI) models have been extensively used in the last 20 years to overcome the limitations of traditional numerical models for GWL simulation [24]. Therefore, improving the planning and management of water resources requires the use of precise soft computing methods for groundwater level (GWL) predictions. Forecasting groundwater levels (GWL) is critical for irrigation planning, water supply, and land development. Although some studies have used artificial neural networks (ANN) [35], support vector machine (SVM) [36] and random forest (RF) [37] methods, support vector machine, generalized regression neural network, convolutional neural network, long short-term memory, and gated recurrent network are examples of machine learning and deep learning methodologies used by others [38] to simulate GWL changes. For example, ref. [11] employed multi-linear regression (MLR) and random forest (RF) methods. ANN models [35] with one natural factor and two anthropogenic factors as input variables were used to anticipate groundwater levels. Furthermore, ANN, SVM, and extreme gradient boosting (XGB), three frequently used machine learning models, were tested for their efficacy in predicting groundwater levels [39]. Ensemble modeling is also a great method to improve the presentation of models. Ensemble machine learning is used to predict GWL changes [40]; however, it is not always the most portable of the ML models [2]. Using a variety of modeling algorithms or training data sets, ensemble modeling is the process of building numerous varied models to predict an outcome [2]. By mixing numerous models instead of just one, ensemble methods offer methodologies that try to increase the accuracy of outcomes in models [36]. The combined models considerably improve the results' accuracy. Due to this, ensemble approaches in machine learning have gained prominence. The goal of employing ensemble models is to lower the prediction's generalization error. When using the ensemble approach, the prediction error lowers as long as the basis models are diverse and independent. Artificial intelligence methods [41], such as genetic programming (GP) and adaptive neural fuzzy inference system (ANFIS) [42], and deep learning models [43], such as LSTM and developed LSTM extension (DeepAR), help in groundwater level forecast and simulation. Furthermore, ref. [44] developed an ensemble of one- to five-month lead-time estimates for water tables based on various data-driven models (DDMs). Furthermore, ref. [6] used a hybrid ANN model to forecast GWL. Models that explicitly mix two or more models are known as hybrid models [45]. The ML architecture that underlies hybrid machine learning (HML) algorithms differs slightly from the conventional workflow. It is understood that every ML algorithm has a strategy for determining the optimal model in the context of an ideal configuration. The radial basis function (RBF) neural network-whale algorithm (WA) model, the multilayer perception (MLP) model, and genetic programming (GP) were the three ANN models used in the same way [46; 47] used ANN and ANFIS for prediction of GWL.