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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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A study of uncertainties in groundwater vulnerability modelling using Bayesian model averaging (BMA).
Maryam Gharekhani1, Ata Allah Nadiri2, Rahman Khatibi3
1Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.
Journal of Environmental Management
|December 11, 2021
Summary
Bayesian Model Averaging (BMA) enhances groundwater vulnerability assessment by combining Artificial Intelligence models. This approach accurately identifies high-uncertainty areas correlating with higher groundwater vulnerability.
Area of Science:
- Environmental Science
- Hydrogeology
- Artificial Intelligence
Background:
- Groundwater vulnerability assessment is crucial for water resource management.
- The Basic DRASTIC Framework (BDF) is a common method, but may have limitations in capturing complex uncertainties.
- Inclusive Multiple Modelling (IMM) strategies can improve assessment accuracy.
Purpose of the Study:
- To assess groundwater vulnerability in the Lake Urmia region using Bayesian Model Averaging (BMA).
- To quantify inherent uncertainties in groundwater vulnerability models.
- To compare the performance of Artificial Intelligence (AI) models within a two-level BMA framework.
Main Methods:
- Development of three Level 1 AI models: Artificial Neural Network (ANN), Gene Expression Programming (GEP), and Support Vector Machines (SVM).
- Application of a Level 2 BMA strategy to combine outputs from Level 1 models, quantifying model uncertainties.
- Validation of model performance using measured nitrate-N concentrations in the aquifer.
Main Results:
- Level 1 models, including BDF, demonstrated high accuracy in the study area.
- The BMA strategy effectively maximized information extraction from local data.
- Higher uncertainties were observed in areas with greater groundwater vulnerability, and vice versa.
Conclusions:
- The combined BMA and AI modeling approach provides a robust method for groundwater vulnerability assessment.
- Uncertainty quantification is directly linked to vulnerability levels, offering valuable insights for water management.
- This integrated strategy enhances the reliability of groundwater vulnerability mapping.
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