Groundwater Level

Groundwater level monitoring and prediction are crucial for effective water resource management and disaster mitigation. Current research emphasizes the application of advanced machine learning techniques, such as U-Net, Vision Transformers, and Gaussian processes combined with neural networks, to model groundwater levels accurately, even with sparse or noisy data. These models aim to improve forecasting accuracy and efficiency, particularly in predicting responses to events like droughts and floods, and to better understand complex interactions between groundwater and surface water. The resulting improvements in predictive capabilities have significant implications for optimizing water usage, preventing water shortages, and mitigating the impacts of extreme weather events.