## 2 Background Literature
### Related Work  
Existing research efforts have sought to review the literature on ML and MM models for GWL predictions, as previously indicated. For example, [PERSON] et al. [68] demonstrated a thorough understanding of the state-of-the-art ML models utilized for GWL modeling as well as the milestones achieved in this domain. Despite their research being beneficial to scholars, it does not answer worries about recent machine learning accomplishments. In comparison to this present study, it is two years behind. [PERSON] et al. [69] presented many state-of-the-art artificial intelligence (AI) methods for groundwater quality (GWQ) modeling, as well as a brief description of common AI methodologies. [PERSON] et al. [70] focused on a high-level overview of critical water quality parameters for a specific use case, as well as the formulation of cost estimates for monitoring them. In a review paper, [PERSON] et al.) [71] reviewed several machine learning and artificial intelligence techniques and methodologies that were commonly used to model and anticipate GWL changes from 2011 to 2020. Other scholars focused their research on measuring model correctness. [PERSON] et al. [72] conducted a systematic review of this subject and evaluated the accuracy of numerous models. According to their statistics, their study was limited to twenty-eight (28) countries. [PERSON] et al. [73] examined current development in GWL strategies based on geographic information systems (GIS) and remote sensing. Their research did not pay attention to machine learning model methodologies. In addition, [PERSON] et al. [74] examined an artificial neural network (ANN) model for groundwater level prediction in a study. Their research is not a systematic review, but it does focus on ANN modeling.  
In addition, traditional groundwater modeling options, such as certain numerical methodologies, have been proposed [75; 76]. Researchers analyzed computing models and simulations for groundwater modeling in a study by [PERSON] et al. [77], but they were concerned about existing data collection methods being able to meet computational model criteria and management objectives. Furthermore, in a study by [PERSON] [78], numerical modeling was used to estimate groundwater levels. They claimed that numerical groundwater modeling is a better alternative to costly aquifer pumping tests for describing aquifer response to external loads. [PERSON] et al. [79; 80] studied three mathematical models to represent variable-density groundwater flow simulations in a systematic investigation.  
They were particularly interested in non-Boussinesq effects. The study, however, was insufficiently thorough in meeting the requirements of the systematic review study.  
Although the studies mentioned above do not represent the entirety of the literature on machine learning and mathematical models for groundwater level prediction, a search of academic electronic databases for systematic review studies on groundwater level prediction using machine learning and mathematical models yielded few results, if any at all. It is critical to be aware of data on publication trends, popular machine learning algorithms and performance metrics, and mathematical models for groundwater level variations. These data are important for researchers because of the knowledge gap they will fill and the possibility for growth in this field. Despite the fact that systematic reviews may not promise bias-free research, they do reduce bias and give auditable results. As a result of this, it is critical to use a systematic approach to investigate GWL change prediction utilizing machine learning and mathematical models.