Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study
Abstract
:1. Introduction
1.1. Machine Learning for Groundwater Prediction
1.2. Study Objectives and Problem Statement
- How will the water table be affected by climate change in the future based on different socio-economic pathways (SSPs)?
- Do ML models perform well enough in predicting changes of the groundwater in Denmark? If so, which ML model outperforms for forecasting these changes?
2. Data and Materials
2.1. Study Area
2.2. Dependent Variable: Jupiter Database
2.3. Independent Variables
3. Methods
3.1. Machine Learning Algorithms
3.1.1. Random Forest
3.1.2. Artificial Neural Networks
3.1.3. Support Vector Machines
3.2. Implementation
4. Results
4.1. Comparison of the Models
4.2. Future Predictions
5. Discussion
5.1. Comparison of the Models
5.2. Future Predictions
5.3. Limitations of the Model
5.4. Limitations of the Data
5.5. Implications to Society and Decision Makers
6. Conclusions
7. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Type/Group | Variable | Type | Resolution | Source |
---|---|---|---|---|
Geology | Clay content (1–4) | Continuous | 30 m | Adhikari et al. [48] |
Depth to clay occurence | Continuous | 30 m | - | |
Soil drainage class | Categorical | 30 m | Møller et al. [49] | |
Soil type | Categorical | N/A | GEUS [50] | |
Topography | DEM | Continuous | 25 m | Copernicus [51] |
Topographic wetness index | Continuous | 25 m | - | |
Flow accumulation | Continuous | 25 m | - | |
Slope | Continuous | 25 m | - | |
Incoming solar radiation | Continuous | 25 m | - | |
Water | Horizontal distance to nearest waterbody | Continuous | 25 m | - |
Vertical distance to nearest water body | Continuous | 25 m | - | |
Water bodies (lakes, streams, etc.) | Categorical | Koch et al. [29] | ||
Sea level | Continuous | N/A | NOAA [52] | |
Land cover | Corine | Categorical | 100 m | Copernicus [53] |
Imperviousness | Continuous | 20 m | Copernicus [54] | |
Bioclimatic variables (monthly historical data) | Precipitation | Continuous | 4.5 km | Harris et al. [55] |
Minimum temperature | Continuous | 4.5 km | ||
Maximum temperature | Continuous | 4.5 km | ||
Average temperature | Continuous | 4.5 km | ||
Coordinates | xytm | Continuous | 25 m | - |
yutm | Continuous | 25 m | - | |
Bioclimatic variables–Future projections | Precipitation | Continuous | 4.5 km | Fick & Hijmans [56] |
Average temperature | Continuous | 4.5 km |
ML Model | R2 | RMSE (m) | MAE (m) |
---|---|---|---|
RF | 0.75 | 0.98 | 0.61 |
ANN | 0.63 | 1.19 | 0.85 |
SVM | 0.65 | 1.15 | 0.75 |
Land Cover Type | R2 | MAE (m) |
---|---|---|
Urban | 0.70 | 0.63 |
Agricultural | 0.65 | 0.69 |
Nature | 0.86 | 0.38 |
Scenario | Winter (%) | Summer (%) |
---|---|---|
2018 | 1.40 | 1.26 |
2.4–5 | 1.40 | 1.25 |
3.7–0 | 1.43 | 1.29 |
5.8–5 | 1.43 | 1.41 |
Winter | Summer | |||||
---|---|---|---|---|---|---|
Scenario | SSP 2.4-5 | SSP 3.7-0 | SSP 5.8-5 | SSP 2.4-5 | SSP 3.7-0 | SSP 5.8-5 |
Max. rise (m) | +0.67 | +0.83 | +0.82 | +0.70 | +0.70 | +0.71 |
Max. fall (m) | −0.52 | −0.47 | −0.49 | −0.52 | −0.49 | −0.52 |
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Gonzalez, R.Q.; Arsanjani, J.J. Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study. ISPRS Int. J. Geo-Inf. 2021, 10, 792. https://doi.org/10.3390/ijgi10110792
Gonzalez RQ, Arsanjani JJ. Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study. ISPRS International Journal of Geo-Information. 2021; 10(11):792. https://doi.org/10.3390/ijgi10110792
Chicago/Turabian StyleGonzalez, Rebeca Quintero, and Jamal Jokar Arsanjani. 2021. "Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study" ISPRS International Journal of Geo-Information 10, no. 11: 792. https://doi.org/10.3390/ijgi10110792