## 4 A Brief Discussion of the Literature Review Assessment
### Deep Learning  
Recent advancements in DL have significantly improved the accuracy and reliability of GWL modelling. A synthesis of twelve prominent studies highlights the diverse methodologies, data inputs, and outcomes:  
* Integrating advanced methods--XGBoost, LSTM, and CNN have revolutionised GWL modelling. For instance, [5] demonstrated the superior performance of XGBoost using a blend of time series, an ensemble-based method, and DL. This ability to integrate meteorological and hydrological datasets resulted in highly accurate 3-month forecasts, showcasing the potential of ensemble-based approaches;
* Innovative model combinations--combining different machine learning architectures with DL, like the SATCN-LSTM proposed by [57], enhances predictive accuracy. Their study utilised self-attention mechanisms alongside LSTM networks to model complex meteorological data, effectively outperforming traditional methods. This indicates that hybrid methods can better capture intricate data patterns;* Long-term data utilisation--[6] leveraged extensive historical piezometric data (50 years) to reconstruct GWL fluctuations;
* Context-specific applications--[58; 59] applied LSTM and SATCN-LSTM to address regional rainfall patterns and groundwater withdrawal scenarios, respectively;
* Handling outliers and noise--[60] tackled the challenge of data anomalies by developing a robust cost function integrated with recurrent ANN methods. This approach effectively managed outliers and noise, enhancing the reliability of GWL modelling. Such methods are crucial for real-world applications where data integrity can be compromised;
* Daily and seasonal predictions--the ability to provide accurate daily and seasonal predictions is exemplified by studies like those of [61; 62]. The first one focused on daily GWL using LSTM with daily precipitation data, while the second recommended DL over ELM and GPR for seasonal groundwater depth predictions;
* Extreme weather event predictions--[63] emphasised accurately predicting groundwater responses to storm events using LSTM. Their study demonstrated that LSTM outperformed traditional RNN in such scenarios, providing critical information for flood management and coastal protection;
* Enhancing model performance with preprocessing--[66] highlighted the significance of comprehensive data preprocessing in improving model performance. Their use of NARX-DNN, LSTM, GRU, and ARX methods on water table level data showed that preprocessing steps are essential for accurately detecting stressed aquifer conditions;
* Superior methods comparisons--comparative studies, such as that of [67], evaluated different methods to identify the most effective ones for predicting water table depth. Their study found that their proposed model outperformed FFNN and Double-LSTM, highlighting the importance of rigorous model comparison in achieving optimal results.  
Advanced and hybrid methods, long-term and context-specific data, robust preprocessing techniques, and tailored applications to regional and temporal conditions have significantly enhanced the accuracy and reliability of GWL predictive modelling.