## 5 Research Legitimacy, Limitations, and Conclusions  
This article presents an overview of existing research on the application of machine learning and mathematical model techniques for modeling groundwater level changes. It is one of the few systematic reviews that we are aware of, if not the first, that considers both machine learning and mathematical model approaches in literature, and also considers their impact on groundwater level changes. However, it is the first in a systematic review to analyze ML and MM techniques in predicting groundwater level changes. Methods validation in research is critical, so we must validate our approach. The data for this study were gathered using the Scopus database. Possible paper omissions during the selection process, as well as data extraction bias (consideration of papers from only the Scopus database), are threats to the study's validity. Considering only the Scopus database, there are a number of other databases, including IEEE, Web of Science, and ACM, that may contain related studies. Our findings in this study cannot be generalized, but they do show how ML and MM techniques have been utilized in the domain to forecast groundwater level changes. Furthermore, in order to reduce bias in paper selection, the PRISMA [81] guidelines were used, which have been shown to be effective, as shown in a work by [PERSON] et al. [82]. Multiple people carefully selected the search string, database, and scope. Furthermore, the protocol was reviewed by domain experts as external reviewers to eliminate any potential bias. The groundwater search keywords were carefully chosen to ensure a descriptive scope and to extract papers for the review. The study only considered journal articles to ensure that the primary studies were of excellent quality and have undergone peer review; while journal articles are not without bias, ensuring quality is critical. Although the omission of conference papers may introduce some bias, it is difficult to select the best ones from the vast number of available proceedings. Researchers assessed the paper quality on conference proceedings at their discretion, which would have added an additional bias, hence the omission of conference papers in this study. In fact, the study demonstrated that various researchers have used ML and MM approaches to predict groundwater level changes. Although both ML and MM techniques are used, according to our primary articles (117 articles), the former is mostly used for the same purpose in recent years, presenting a significant skewness in the domain. The state of the water as well as its level are interesting aspects of groundwater modeling (e.g., concentrations of different chemicals and geospatial analysis). Machine learning has lowered computational complexity and shortened model training times. Furthermore, researchers frequently used the support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms in their studies, with little emphasis on other algorithms such as ensembles, hybrid, and metaheuristic algorithms. Researchers are encouraged to use mathematical model approaches to predict changes in groundwater levels. Because these algorithms are rarely used, it is both a difficulty and an opportunity for scholars and stakeholders in this domain to investigate new ML and MM techniques. Both ML and MM techniques are anticipated to continue to deliver opportunities for modeling groundwater level changes. Researchers must begin adopting and adapting new approaches, as well as publishing widely, to ensure that research does not become static. Groundwater is important in people's daily lives, and it is threatened by global climate change. Researchers, governments, organizations, non-governmental organizations (NGOs), and other stakeholders are encouraged to advocate for climate change mitigation and education. Our research has demonstrated that machine learning techniques are more recently used for modeling changes in groundwater levels than mathematical models.