Groundwater Flow

Groundwater flow research focuses on understanding and predicting the movement of water beneath the Earth's surface, primarily to manage water resources and mitigate contamination. Current research heavily utilizes machine learning, employing architectures like U-Nets, convolutional neural networks, and Gaussian processes, often integrated with physics-based models to improve accuracy and efficiency, particularly in scenarios with limited data. These advancements are crucial for optimizing agricultural practices (e.g., managed aquifer recharge), improving groundwater level estimations, and developing effective remediation strategies for contaminated aquifers, ultimately contributing to sustainable water management and environmental protection.