## 7 Final Remarks  
The performed comprehensive literature review, covering the time interval 2001-2023, highlights the use of AI-based methods in GWL modelling. The examination of the different studies shows several critical results for the most used methodological approaches, as follows:  
* ANNs and other advanced algorithms, such as FFANN, RNN, and ELM, have significantly improved the accuracy and reliability of GWL modelling. Compared to traditional statistical models, these methods better capture complex patterns and nonlinear relationships between input and output variables. In addition, including different input variables and optimising the algorithms contribute to improving the prediction accuracy;
* ANFISs are robust methods for GWL prediction and outperform others such as ANNs, SVMs and GP in various analysed papers. ANFISs can account for different environmental data and provide higher accuracy than alternative methods. Combining fuzzy logic with neural networks shows higher accuracy, especially for non-stationary time series data. The choice of membership functions significantly impacts the accuracy of the constructed GWL prediction models;
* SVMs are superior in predicting GWL as they can effectively handle complex and heterogeneous data. This method is characterised by its robustness in dealing with incomplete data and outliers. SVMs, especially those using the RBF kernel algorithm, are excellent at capturing the complexity of GWL;
* Recent advances in DL methods, including XGBoost, LSTM, and CNN, have significantly improved the accuracy and reliability of GWL modelling. These methods are characterised by their ability to integrate different data sources, provide accurate forecasts for different temporal and spatial scales, and cope with extreme weather events;
* GP and GEP methods are powerful tools for groundwater table modelling. GP is characterised by high flexibility and accuracy and allows the modelling of complex and nonlinear relationships in groundwater data. In addition, including optimisation methods and uncertainty analyses increases the reliability and robustness of the predictions;
* Hybrid methods combine different modelling approaches and optimisation strategies and have a higher forecasting capability. These methods utilise the strengths of different approaches and incorporate different data sources to improve forecast accuracy and reliability. In addition, including uncertainty analyses and adapting models to specific local and regional environmental conditions help advance GWL modelling. The preceding literature review emphasises the transformative impact of AI-based methods and Hybrid Algorithms on GWL modelling. These advancements offer valuable insights and tools for effective groundwater planning, management, and sustainable water resource utilisation in various environmental and territorial contexts. AI-based methods for GWL modelling contribute to improved predictive accuracy, support adaptive and sustainable management practises, particularly in arid and semi-arid regions, and play a crucial role in addressing challenges related to groundwater sustainability in the face of climate change and increasing water demands. Continued research and investment in AI are vital to ensure effective and sustainable management of essential groundwater resources. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14167358/s1](https://www.mdpi.com/article/10.3390/app14167358/s1)]([https://www.mdpi.com/article/10.3390/app14167358/s1](https://www.mdpi.com/article/10.3390/app14167358/s1)), Table S1. Summary of articles and methods that used ANN. Table S2. Summary of articles and methods that used ANFIS. Table S3. Summary of articles and methods that used DL. Table S5. Summary of articles and methods that used GP. Table S6. Summary of articles and methods that used Hybrid Algorithms. References [1-12] are cited in the supplementary materials  
Conceptualisation, S.P. ; Data curation, S.P. ; Formal analysis, S.P., M.K., and L.A.D.