Spatial Mapping and Prediction of Groundwater Quality Using Ensemble Learning Models and SHapley Additive exPlanations with Spatial Uncertainty Analysis
Abstract
:1. Introduction
2. Study Area
3. Methodology
3.1. Groundwater Samples Descriptions
3.2. GWQ Assessment
3.2.1. EWQI Calculation
3.2.2. Parameter Analysis
3.3. GWQ Mapping and Prediction
3.3.1. Data Split
3.3.2. Indicator Selection by PSR Framework
Potential Pressure Indicators
State Indicators
Potential Response Indicators
3.3.3. Correlation Analysis
3.3.4. LightGBM Model
3.3.5. Hyperparameter Selection and Optimization
3.3.6. Model Performance Evaluation
3.4. Spatial Uncertainty Analysis
3.5. Indicator Importance Analysis and SHAP Analysis
4. Results
4.1. GWQ Assessment Results
4.2. Indicator Selection by Correlation Analysis
4.3. Optimal Hyperparameters, Model Performance, and Spatial GWQ Mapping
4.4. Spatial Uncertainty in GWQ Mapping
4.5. Indicator Analysis with Importance and SHAP Value
5. Discussion
5.1. Discussion on GWQ Assessment
5.2. Model Performance and Spatial Uncertainty
5.3. SHAP Observation and Discussion
5.4. Limitations and Future Research
- When evaluating GWQ, it is recommended to use multiple methods, including the EWQI, the CPI, and the Nemerow index, and to promote the single parameter analysis method of the NI proposed in this study.
- It is encouraging to confirm the causal relationships between indicators and between the indicators and outcomes, ensuring that the associations identified through SHAP analysis are supported by robust evidence.
- Introduce and compare more models, including deep learning, reinforcement learning, and ensemble learning, to enhance the stability and accuracy of the results.
- Further promote the contribution of the PSR framework in spatial mapping and prediction for indicator selection to ensure the completeness of model construction.
- In addition to calculating spatial average probabilities and supplementing with additional groundwater samples, develop more methods to reduce spatial uncertainty to provide managers with more accurate mapping results.
- Further develop the application of EMLTs in groundwater management.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. The Meaning of Hyperparameters in LightGBM Model
Hyperparameters | Meanings |
---|---|
bagging_fraction | This parameter specifies the fraction of data to be randomly selected for each iteration, which helps in preventing overfitting. |
bagging_freq | This defines how frequently (in terms of iterations) bagging is performed. For instance, setting it to 5 means that bagging is applied every five iterations. |
boosting_type | This parameter determines the type of boosting algorithm to use. |
feature_fraction | This controls the fraction of features (columns) to be randomly selected for each iteration, helping to improve model generalization. |
learning_rate | This is the step size that controls how much the model is adjusted with each iteration, balancing the trade-off between model accuracy and training time. |
num_leaves | This specifies the maximum number of leaves in one tree, which directly impacts the complexity and accuracy of the model. |
Appendix B. The Results of Model Performance after Removing Population and Nighttime Light
Performance Metrics | Test 1 | Test 2 | Test 3 | Test 4 |
---|---|---|---|---|
AUROC | 0.8348 | 0.8534 | 0.8673 | 0.8380 |
Precision | 0.8333 | 0.9167 | 0.7778 | 0.7857 |
Recall | 0.6 | 0.6111 | 0.7778 | 0.6111 |
F1 score | 0.6977 | 0.7333 | 0.7778 | 0.6875 |
Overall accuracy | 0.6389 | 0.7778 | 0.7778 | 0.7222 |
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Parameters | Min | Max | Mean | SD | Standard |
---|---|---|---|---|---|
pH | 6.96 | 9.89 | 7.84 | 0.32 | 6.5–8.5 |
Total Hardness (TH) | 9 | 1885 | 478.87 | 291.5 | 450 |
Total Dissolved Solids (TDS) | 196 | 10570 | 1077.61 | 1098.60 | 1000 |
Calcium (Ca2+) | 0.56 | 301 | 92.58 | 55.91 | 75 |
Magnesium (Mg2+) | 1.8 | 352 | 60.24 | 53.36 | 30 |
Potassium (K+) | 0.13 | 49.5 | 3.17 | 5.82 | 12 |
Sodium (Na+) | 6.36 | 1160 | 140.62 | 155.88 | 200 |
Chloride (Cl−) | 3.7 | 2135 | 106.54 | 195.99 | 250 |
Sulfate (SO42−) | 1.33 | 4255 | 230.9 | 434.04 | 250 |
Bicarbonate (HCO3−) | 117 | 1349 | 509.41 | 189.46 | 300 |
Nitrate (NO3−) | 0 | 373 | 47.32 | 53.59 | 20 |
Fluoride (F−) | 0.12 | 4.26 | 0.98 | 0.77 | 1 |
Zinc (Zn2+) | 0.001 | 0.066 | 0.008 | 0.008 | 1 |
Hexavalent chromium (Cr6+) | 0.001 | 0.45 | 0.033 | 0.055 | 0.05 |
Aluminum (Al3+) | 0.003 | 0.1 | 0.008 | 0.01 | 0.2 |
Iron (Fe3+) | 0 | 0.35 | 0.105 | 0.074 | 0.3 |
Group | Indicators | Sources | Scale | Format |
---|---|---|---|---|
Pressure | Population | SEDAC | 250 m | Raster |
PPSD | SPDEE | 1:300,000 | Point | |
LULC | Yang and Huang [73] | 30 m | Raster | |
State | Depth to groundwater | MWRPIC | 1:300,000 | Line |
Net recharge | Peng et al. [74] | 1 km | Raster | |
Aquifer water yield capacity | Hydrogeological map | 1:300,000 | Polygon | |
Slope | NASADEM data | 30 m | Raster | |
Impact of the vadose zone | Zhang et al. [55] | 1:300,000 | Polygon | |
Conductivity | Zhang et al. [55] | 1:300,000 | Polygon | |
Potential response | GDP2015 | GRDC | 1 km | Raster |
Ten years change of NDVI | Yang et al. [75] | 30 m | Raster | |
Degree of urbanization | SEDAC | 250 m | Raster | |
Nighttime lights | Elvidge et al. [76] | 1 km | Raster |
Hyperparameters | Hyperparameter Spaces |
---|---|
bagging_fraction | hp.uniform(‘bagging_fraction’, 0.5, 0.9) |
bagging_freq | hp.choice(“bagging_freq”, range(4, 7)) |
boosting_type | hp.choice(“boosting_type”, [‘gbdt’, ‘dart’, ‘rf’]) |
feature_fraction | hp.uniform(‘feature_fraction’, 0.5, 0.9) |
learning_rate | hp.uniform(‘learning_rate’, 0.01, 0.5) |
num_leaves | hp.choice(“num_leaves”, range(15, 128)) |
Parameters | Weights | Exceedance Rate | NI |
---|---|---|---|
pH | 0.008052 | 38.89% | 0.015521 |
Total Hardness (TH) | 0.021665 | 34.44% | 0.036983 |
TDS | 0.062166 | 1.67% | 0.005146 |
Calcium (Ca2+) | 0.024363 | 60.00% | 0.072454 |
Magnesium (Mg2+) | 0.041843 | 68.89% | 0.142876 |
Potassium (K+) | 0.104982 | 4.44% | 0.023104 |
Sodium (Na+) | 0.068902 | 27.78% | 0.094873 |
Chloride (Cl−) | 0.105335 | 7.22% | 0.037696 |
Sulfate (SO42−) | 0.107567 | 20.56% | 0.109618 |
Bicarbonate (HCO3−) | 0.014792 | 90.00% | 0.065986 |
Nitrate (NO3−) | 0.067453 | 62.22% | 0.208023 |
Fluoride (F−) | 0.042884 | 35.56% | 0.075585 |
Zinc (Zn2+) | 0.064638 | 0.00% | 0 |
Hexavalent chromium (Cr6+) | 0.124156 | 17.78% | 0.109416 |
Aluminum (Al3+) | 0.108349 | 0.00% | 0 |
Iron (Fe3+) | 0.032851 | 1.67% | 0.002719 |
Selection No. | 65/35 | 70/30 | 75/25 | 80/20 | 85/15 | 90/10 |
---|---|---|---|---|---|---|
1 | 0.8291 | 0.823 | 0.8856 | 0.9074 | 0.8797 | 1 |
2 | 0.8291 | 0.8697 | 0.8573 | 0.8981 | 0.8571 | 1 |
3 | 0.7954 | 0.8011 | 0.8855 | 0.8858 | 0.8546 | 0.9383 |
4 | 0.7808 | 0.823 | 0.8601 | 0.9043 | 0.8731 | 0.9877 |
Average | 0.8086 | 0.8292 | 0.8721 | 0.8989 | 0.8661 | 0.9815 |
SD | 0.0211 | 0.0250 | 0.0135 | 0.0083 | 0.0106 | 0.0254 |
Hyperparameters | Selection 1 | Selection 2 | Selection 3 | Selection 4 |
---|---|---|---|---|
bagging_fraction | 0.755129 | 0.578798 | 0.628146 | 0.817471 |
bagging_freq | 5 | 4 | 5 | 5 |
boosting_type | gbdt | gbdt | gbdt | gbdt |
feature_fraction | 0.861003 | 0.561902 | 0.899878 | 0.677141 |
learning_rate | 0.310296 | 0.069457 | 0.353949 | 0.314827 |
num_leaves | 102 | 45 | 47 | 21 |
Areas | Selection 1 | Selection 2 | Selection 3 | Selection 4 |
---|---|---|---|---|
Very high (km2) | 2625.53 | 1278.48 | 1984.58 | 2261.95 |
High (km2) | 3736.37 | 4915.00 | 3261.57 | 3981.42 |
Moderate (km2) | 3559.01 | 6026.96 | 4728.50 | 5181.86 |
Very low (km2) | 5242.62 | 4353.83 | 4695.23 | 4305.54 |
Low (km2) | 3791.72 | 2380.98 | 4285.36 | 3224.49 |
Indicators | Accumulated Importance | Rank | Proportion |
---|---|---|---|
Population | 74.22 | 1 | 18.55% |
Nighttime light | 70.59 | 2 | 17.65% |
Aquifer media | 41 | 3 | 10.25% |
GDP2015 | 39.49 | 4 | 9.87% |
Groundwater yield | 34.37 | 5 | 8.59% |
Conductivity | 31.23 | 6 | 7.81% |
Change of NDVI | 28.85 | 7 | 7.21% |
Depth to groundwater | 25.21 | 8 | 6.30% |
PPSD | 21.32 | 9 | 5.33% |
Topography | 15.75 | 10 | 3.94% |
LULC | 9.29 | 11 | 2.32% |
Degree of urbanization | 5.99 | 12 | 1.50% |
Net recharge | 2.7 | 13 | 0.67% |
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Yang, S.; Luo, D.; Tan, J.; Li, S.; Song, X.; Xiong, R.; Wang, J.; Ma, C.; Xiong, H. Spatial Mapping and Prediction of Groundwater Quality Using Ensemble Learning Models and SHapley Additive exPlanations with Spatial Uncertainty Analysis. Water 2024, 16, 2375. https://doi.org/10.3390/w16172375
Yang S, Luo D, Tan J, Li S, Song X, Xiong R, Wang J, Ma C, Xiong H. Spatial Mapping and Prediction of Groundwater Quality Using Ensemble Learning Models and SHapley Additive exPlanations with Spatial Uncertainty Analysis. Water. 2024; 16(17):2375. https://doi.org/10.3390/w16172375
Chicago/Turabian StyleYang, Shilong, Danyuan Luo, Jiayao Tan, Shuyi Li, Xiaoqing Song, Ruihan Xiong, Jinghan Wang, Chuanming Ma, and Hanxiang Xiong. 2024. "Spatial Mapping and Prediction of Groundwater Quality Using Ensemble Learning Models and SHapley Additive exPlanations with Spatial Uncertainty Analysis" Water 16, no. 17: 2375. https://doi.org/10.3390/w16172375