Transforming groundwater sustainability, management and development through deep learning

Groundwater (GW) availability is at risk due to over-extraction, pollution, and climate change, despite their vital role in satisfying the world's freshwater needs. Decisions made using outdated, under-data-driven models for groundwater management are not always the best option. Traditional approaches often fail to tackle groundwater systems' intricacies and ever-changing nature, even if groundwater management has come a long way. Groundwater over-extraction, pollution, and depletion are consequences of ineffective monitoring, prediction, and management, which endangers environmental sustainability and water security.

The rising problems of Sustainable Groundwater Management (SGM) and development in the context of global freshwater demand and climate change were addressed in this study. A revolutionary technique for predicting models and the Water Quality Assessment (WQA) by employing the potential of Deep Learning (DL), a type of Artificial Intelligence (AI). Then, the limitations faced by the existing Groundwater Management methods (GM) in predicting the Variations in the GW levels were identified, and it also predicted the quick detection of the WQ (Water Quality) deterioration. The application of DL algorithms offers precise prediction and early detection, and this study also aims to fill the gaps by executing DL on past and present data. By addressing the drawbacks of these traditional methods, Pattern Recognition (PR) and analysis in the DL can revolutionize these procedures. For predicting the modelling of GW levels, the Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and particularly Long Short Term Memory (LSTM) networks are employed in this study.

For WQA, Deep CNN (DCNN) are employed. The hidden patterns are revealed within the large datasets by applying Deep Transformer Analysis (DTA), which supports specific management approaches. The outcomes demonstrated the revolutionary impact of DL techniques. The LSTM networks facilitated the precise predictions for GW variations and Proactive Resource management. CNN accurately determined the GQA, detecting indicators like PH and level of pollutants early. The DTA contributed to classifying the GW quality levels effectively and optimizing the management techniques. The precise predictive models for GW level variations and accurate WQA parameters were presented in this study by applying these advanced techniques to historical and real-time data. The proactive resource management, early detection capabilities, and sustainability of GW resources facilitate the transformative potential of DL and the outcomes obtained. The enhanced accuracy rate of 97.2%, F1 score rate of 96.2%, MAE (Mean Absolute Error) rate of 0.8%, RMSE (Root Mean Square Error) of 1.1%, loss rate of 0.04% were attained by the suggested CNN-DTA model when compared to other current techniques.