Enterprise AI Analysis: Groundwater quality index prediction and aquifer failure risk analysis using metaheuristic-tuned artificial neural networks
Groundwater quality index prediction and aquifer failure risk analysis using metaheuristic-tuned artificial neural networks
Authors: Jafar Jafari-Asl & Sedigheh Mohamadi
This study addresses the critical challenge of groundwater quality assessment and aquifer failure risk in arid and semi-arid regions, focusing on the Jiroft Plain aquifer in southeastern Iran. We developed a hybrid framework combining feedforward neural networks (FFNN) with metaheuristic optimization algorithms, specifically the Artificial Hummingbird Optimization (AHO) algorithm, to construct an accurate Groundwater Quality Index (GQI) prediction model. Utilizing 14 years of groundwater quality data, the model was trained and validated, then integrated with Monte Carlo simulation to generate 1.9 million synthetic samples for probabilistic analysis. Our findings reveal that accounting for uncertainty substantially increases the estimated vulnerability of the aquifer and reduces its reliability to levels below the permissible threshold, indicating a critical groundwater condition. The proposed probabilistic framework proves to be a robust and reliable tool for assessing the true status of aquifers under uncertainty, offering crucial insights for sustainable water resource management.
Keywords: Machine learning, Groundwater resources, Uncertainty quantification, Monte Carlo simulation, Metaheuristics
Executive Impact at a Glance
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Predictive Accuracy (R²)
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Aquifer Reliability (GQIsafe=300)
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Monte Carlo Samples
Key Executive Takeaways
- A novel hybrid framework integrates FFNN with metaheuristic optimization (AHO) for GQI prediction.
- Uncertainty quantification via Monte Carlo simulation reveals significantly increased aquifer vulnerability.
- Aquifer reliability is reduced to critical levels (66% at GQIsafe=300) when uncertainty is considered.
- The AHO algorithm demonstrated superior predictive accuracy (R²=0.999, MSE=8.833E-05) compared to other ML models.
- The framework offers a robust decision-support tool for sustainable groundwater management, enabling early warning and targeted monitoring.
Deep Analysis & Enterprise Applications
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This study addresses the critical challenge of groundwater quality assessment and aquifer failure risk in arid and semi-arid regions, focusing on the Jiroft Plain aquifer in southeastern Iran. We developed a hybrid framework combining feedforward neural networks (FFNN) with metaheuristic optimization algorithms, specifically the Artificial Hummingbird Optimization (AHO) algorithm, to construct an accurate Groundwater Quality Index (GQI) prediction model. Utilizing 14 years of groundwater quality data, the model was trained and validated, then integrated with Monte Carlo simulation to generate 1.9 million synthetic samples for probabilistic analysis. Our findings reveal that accounting for uncertainty substantially increases the estimated vulnerability of the aquifer and reduces its reliability to levels below the permissible threshold, indicating a critical groundwater condition. The proposed probabilistic framework proves to be a robust and reliable tool for assessing the true status of aquifers under uncertainty, offering crucial insights for sustainable water resource management.
Aquifer Failure Risk Assessment Methodology
The proposed probabilistic framework for aquifer failure risk assessment, using the ML approach in conjunction with MCS, involves the following key steps.
Generate random variables as inputs for the ML
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Use ML to estimate the GQI
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Limit State Function Evaluation
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Probability of Failure Calculation
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Aquifer Reliability Calculation
AHO Algorithm Performance Highlight
The Artificial Hummingbird Optimization (AHO) algorithm significantly outperformed other metaheuristic optimizers in training the FFNN for GQI prediction.
8.833E-05 Lowest Average MSE (Training)AHO exhibited the lowest Mean Squared Error, signifying the highest accuracy and stability in the training phase, with the smallest standard deviation of 2.228E-05.
| Model | R² | RMSE | MAE |
|---|---|---|---|
| AHO-FFNN | 0.99 | 9.756E+00 | 9.250E+00 |
| MPA-FFNN | 0.99 | 1.025E+01 | 9.279E+00 |
| SVR | 0.98 | 1.668E+01 | 1.546E+01 |
| RBF | 0.97 | 1.915E+01 | 1.346E+01 |
| Gradient-FFNN | 0.95 | 1.461E+01 | 1.123E+01 |
Jiroft Plain Aquifer: Impact of Uncertainty
The Jiroft Plain aquifer provides a critical case study demonstrating the impact of uncertainty on groundwater quality assessment, revealing significant degradation.
- Location: Jiroft Plain, Southeastern Iran
- Challenge: Decline in groundwater quality and reliability due to overexploitation and reduced recharge.
- Key Findings:
- In 2009, 29% of the aquifer was 'good'; by 2023, it declined to 14.36%.
- The 'poor' category increased from 68.49% to 83.02% by 2023.
- Under parameter uncertainty, 99.93% of the aquifer falls into 'very poor' category, and 0.07% is 'unsuitable for drinking'.
- This significant shift highlights the high sensitivity and vulnerability of the aquifer when uncertainty is considered.
- Impact: Uncertainty substantially increases the estimated vulnerability and reduces reliability to critical levels, necessitating robust management strategies.
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