## 3 Artificial Intelligence Methods for GWL Modelling
### Hybrid Algorithms  
Integrating AI with hybrid optimisation algorithms has proven effective in overcoming challenges associated with traditional methods in GWL modelling [75]. Notable achievements include the development of robust methods enhancing accuracy, particularly in arid and semi-arid regions [1; 76]. Incorporating hybrid evolutionary algorithms, including the Non-Dominated Sorting Genetic Algorithm (NSGA)-II, Multi-Objective Particle Swarm Optimisation (MOPSO), and Whale Algorithm, contributes to model resilience, addressing  
Figure 12: Graphical representation of the trend in the number of scientific articles published between 2011 and 2023 on using GP in groundwater studies. The size of the dots indicates the number of articles related to each country. The larger the number of articles, the bigger the dot. issues like over- and underestimation [77; 78]. Advanced methods improve prediction accuracy and prove resource-efficient, especially in areas with limited statistical data or challenges in employing complex numerical models [79; 80]. This study analyses 35 articles published between 2010 and 2023, providing an assessment of each article followed by a general conclusion drawn from them (Figure 13). Ref. [76] Introduced a novel multi-layer perceptron learning process, which included a Non-Dominated Sorting Genetic Algorithm (2 NSGA II) and Multi-Objective Particle Swarm Optimisation (MOPSO) for GWL prediction in Yazd, Iran. By merging multiple evolutionary algorithms, including NSGA II and MOPSO, the researchers aimed to enhance prediction accuracy. The input variables comprised temperature, precipitation, and GWL datasets. This research emphasises the significance of tailored modelling for addressing water resource challenges in arid regions. Insights from this study pave the way for advancing groundwater management pracises in similar climatic conditions. Ref. [3] investigated hybrid wavelet-machine learning methods for GWL prediction in Saveh, Iran, focusing on the WT-LSSVM. Through extensive experimentation with various input parameters like precipitation, evapotranspiration, temperature, and groundwater level datasets, this study demonstrates the superior performance of the WT-LSSVM model inaccurate forecasting. Advanced modelling methods enhance water resource sustainability and resilience in similar geographic contexts. Ref. [79] present the outlier-robust Extreme Learning Machine (ORELM) method as a promising GWL prediction alternative. Through comparative analysis with conventional methods like the Groundwater Model System (GMS), Genetic Algorithm (GA)-ANN, Imperialist Competitive Algorithm (ICA)-ANN Hybrid Algorithms, ELM, and ORELM, the research highlights the ORELM model's superior accuracy, efficiency, and cost-effectiveness. Ref. [1] contribute to groundwater research by establishing the effectiveness of Hybrid Algorithms for predicting GWL. Focusing on the Shazand Plain, Iran, this study compares various hybrid methods such as PSO-ANN, ACA-ANN, LSTM, LS-SVM, ORELM, and the GMS. By utilising monthly GWL datasets and advanced approaches, this research underlines the importance of adopting tailored methodologies for accurate groundwater forecasting. Ref. [80] introduce the Augmented Artificial Ecosystem Optimisation-based Multi-Layer Perceptron (AAEO-MLP) for monthly GWL forecasting. Situated in Karnataka, India, this study showcases the model's superior accuracy, convergence, and stability performance compared to other forecasting methods. Ref. [81]  
Figure 13: Graphical representation of the trend in the number of scientific articles published between 2010 and 2023 using hybrid methods in groundwater studies. The size of the dots indicates the number of articles related to each country. The larger the number of articles, the bigger the dot. investigate the ANFIS Grasshopper Optimisation Algorithm (GOA) for GWL prediction, focusing on the Ardebil Plain, Iran.