A data-driven approach to exploring the causal relationships between distributed pumping activities and aquifer
Min Pang1, Erhu Du2, Chunmiao Zheng3
1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, China; College of Hydrology and Water Resources, Hohai University, Nanjing, China.
This study introduces a new AI framework to track groundwater depletion caused by multiple users. It accurately quantifies each stakeholder's impact on aquifer levels, aiding sustainable water management.
Area of Science:
- Hydrology
- Data Science
- Environmental Science
Background:
- Groundwater depletion from distributed pumping causes significant environmental issues globally.
- Traditional models (well hydraulics, hydrological models) have limitations due to simplifying assumptions or data uncertainties.
Purpose of the Study:
- To develop a novel modeling framework for assessing aquifer depletion without traditional hydrological models.
- To quantify the causal effects of individual pumping activities on groundwater depletion.
Main Methods:
- Integration of a deep learning model for spatiotemporal aquifer dynamics.
- Application of statistical causal inference to identify stakeholder networks and causal strengths.
Main Results:
- Deep learning model achieved over 90% accuracy in capturing water table dynamics.
- The framework successfully identified causal networks between pumping and water table drawdown.
- Quantified individual stakeholder contributions to aquifer depletion.
Conclusions:
- The novel framework effectively assesses individual impacts on groundwater depletion.
- This approach can resolve externality issues in common-pool groundwater management.
- Provides a data-driven solution for sustainable aquifer management.
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