## 5 Management Implications  
Applying AI-based methods for GWL predictive modelling has profound implications for water resource planning, management, and decision-making policies. The insights derived from these advanced methodologies offer significant potential for improving the sustainability and efficiency of groundwater resources. The enhanced predictive accuracy is crucial for developing proactive groundwater management strategies, particularly in regions facing severe water scarcity and fluctuating water tables. Implementing the discussed AI-based methods facilitates the integration of real-time data and long-term predictions into groundwater management frameworks. This capability allows for more dynamic and adaptive management practises, ensuring that water extraction rates are aligned with the current and forecasted groundwater availability, thereby preventing over-extraction and safeguarding sustainable use [69, 74]. Machine learning methods, such as ANNs and SVMs, in GWL studies can enhance cartographic models, improving their predictive accuracy and supporting more effective environmental strategies. The ability of the ANFIS method to handle uncertainty and capture complex relationships makes it essential for addressing the challenges associated with groundwater management and conservation [41]. For example, DL's effectiveness in addressing hydrological forecasting challenges is crucial for optimising weather station networks and improving groundwater management practises, especially in arid and semi-arid regions where water resource sustainability is critical [73]. This last method is beneficial for areas facing significant groundwater contamination issues, as it helps in effectively identifying and mitigating sources of pollution. From the strategic placement of weather stations to uncertainty analysis, GP methods also play a significant role in developing sustainable groundwater management practises. Furthermore, Hybrid Algorithms that combine AI-based methods with traditional statistics have shown promise in areas with limited environmental data. These hybrid approaches enhance the robustness and reliability of GWL modelling, making them valuable tools for decision-makers in developing targeted interventions and policies that are resilient to data scarcity and environmental variability [1, 75, 76]. For example, [79] advocate adopting Hybrid Algorithms to improve groundwater management strategies in diverse hydrogeological settings. Integrating optimisation algorithms with ANN, ANFIS, and SVM methods provides valuable insights into improving groundwater management strategies by adopting more robust and reliable hybrid modelling methods [85]. AI-based management methods can help develop warning systems for droughs and groundwater depletion. These systems provide accurate predictions and timely alerts to authorities and communities about impending water shortages. This allows for the implementation of mitigation measures well in advance [68, 71]. Additionally, AI-based models can optimise the allocation of groundwater resources for various purposes, such as agricultural irrigation, industrial processes, and domestic consumption. By simulating different usage scenarios, policymakers can assess the potential impacts of varying allocation strategies and choose the most efficient and fair distribution plans [72, 77]. Integrating AI-based GWL modelling into water resource planning and management practises offers numerous benefits, including improved prediction accuracy, enhanced decision-making capabilities, and the development of more sustainable strategies. As these methods advance, their applications in groundwater management are expected to become increasingly sophisticated, providing crucial support for addressing the complex challenges of water resource sustainability.