Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and...

Area of Science:

• Hydrology
• Environmental Science
• Data Science

Background:

• Groundwater models are crucial for water resource management but face challenges like uncertainty and computational limits.
• Machine intelligence, particularly deep learning, offers potential solutions but struggles with interpretability and generalization.
• Existing methods often rely heavily on data quantity, overlooking data quality and integration with human knowledge.

Purpose of the Study:

• To develop a novel ternary framework integrating multisource data, human expertise, and machine intelligence for enhanced groundwater modeling.
• To address limitations in current machine-driven approaches, focusing on interpretability and generalizability.
• To advance sustainable water management through improved understanding and prediction of groundwater systems.

Main Methods:

• Development of a synergistic ternary framework combining data, human intellect, and machine intelligence.
• Strategic utilization of multisource data, emphasizing quality over sheer volume.
• Integration of advanced computing techniques with expert knowledge for groundwater simulation.

Main Results:

• The proposed framework overcomes limitations of data-only or machine-only approaches.
• Enhanced interpretability and generalizability in machine-driven groundwater simulations.
• Improved predictive capabilities for groundwater systems through a holistic approach.

Conclusions:

• The ternary framework offers a pathway to more realistic and reliable groundwater simulations.
• This approach supports sustainable water management by enhancing understanding and prediction.
• Synergizing data, human expertise, and machine intelligence is key to overcoming current groundwater modeling challenges.