Bridging data scarcity in groundwater quality studies: A systematic evaluation of statistical and deep learning-based generators

Key Points

• Groundwater quality studies face significant data scarcity issues, impacting analyses and interventions.
• The evaluation distinguishes between statistical and deep learning-based generators, with deep learning showing greater promise.
• Systematic review across various datasets highlights the effectiveness of these generators in filling data gaps.
• Implications indicate a need for integrating advanced statistical methods in groundwater management.