## 2 Methodology of the Research  
In the first stage of this research, a comprehensive review of GWL forecasting has been explored and analyzed. A few major scientific research databases, Web of Science, Scopus, etc., were decided to organize the research. The papers with the word \"survey\" or \"review\" in the keyword or abstract are reviewed. The majority of the papers were examined on GWL and selected to cite. The only available research papers on GWL prediction were studied and were chosen for our research. Once these research papers are analyzed, numerous studies are published every year. [PERSON] et al. [30] surveyed 78 articles, and [PERSON] et al. [31] surveyed 318 articles. As far as we know, no comprehensive study on GWL prediction is available using deep learning. These two review articles were published this year and are growing in popularity as more new research is published. Data processing and the separation of training and testing are not included in the analysis. As the global climate continues to change, recent studies of the GWL model have used new kinds of data and applied different methods. For this reason, it is essential to consider the latest algorithms and methods, including deep learning algorithms and hybrid algorithms, along with the proprietary processing methods applied. After the analysis of available databases was concluded, the search equation was identified as the latest and very significant equation for GWL prediction. We explore the available databases to update the latest trends and analyses. After examining the online searched databases, more than 731 papers suit the search strategy. A total of 182 of these papers were rejected, and 549 other papers were excluded from this review because their main objective was not GWL prediction. After studying the most relevant papers, the analysis was then conducted. Figure 1 shows the arithmetic conceptualization of GWL research using an AI-based model during 2008-2022. Several papers were chosen based on specific measures. The main objectives of this research are  
1. To discuss the conventional methodology for GWL. 2. To explore the current GWL methodologies. 3. Deep-learning-based models for GWL. 4. Machine -learning-perspectives-based groundwater modeling. We used different searching keywords to find relevant studies: Set 1: \"GWL\" [32], \"Ground-Water-level\" [33]; and \"Groundwater Level prediction\" [34]; set 2: \"Prediction\", \"forecasting\", \"Deep Learning\", \"analysis\", \"estimation\". We used the keyword AND between set1 and set2, and the OR operator was used between keywords in a set. Figure 2 illustrates the relevant and irrelevant papers selection process. Once read through the database, 731 papers met the search criteria. One hundred and eighty-two (182) duplicates were excluded from this analysis. After reviewing the titles and journals, 440 were excluded from the review because they did not go through the GWL criteria. After a thorough reading of these articles, 109 articles were finally analyzed. All relevant papers were selected where the GWL, the data collection time, and the research project variables were tested. Many studies have used technological and water variables to model GWL. However, some studies have considered other factors to measure GWL, such as tree rings diameter, climatic conditions, area, change in population, duration, and the size of the data collection time. Figure 1: Arithmetic conceptualization of GWL research using AI-based model during 2008–2022. Figure 2: Relevant and irrelevant papers selection process. elevation, land use data, paved area, and so on. The research results are classified and analyzed in the next section based on the variables used for GWL modeling. Groundwater is a primary source of water for living things around the globe. Large urban areas generate enormous demands for water and food. India, Iran, and China are the main countries in the GWL study. Of the 20 countries surveyed, about half of the studies took place in India, Iran, and China.