Predicting Groundwater Level Based on Machine Learning: A Case Study of the Hebei Plain
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Methods
2.3.1. Support Vector Machine
2.3.2. Long-Short Term Memory
2.3.3. Gated Recurrent Units
2.3.4. Multi-Layer Perceptron
2.4. Performance Evaluation of Models
2.5. Groundwater Level Prediction Methodology
3. Results and Discussion
3.1. GWL Prediction Using SVM Model
3.2. GWL Prediction Using the LSTM Model
3.3. GWL Prediction Using the MLP Model
3.4. GWL Prediction Using the GRU Model
3.5. Model Comparison
4. Conclusions
- (1)
- By comparing the RMSE, R2, and NSE indicators, we discovered that the GRU model performed the best for dynamically fluctuating and dynamically increasing stations, while the MLP model performed the best for dynamically decreasing stations. The update gate in the GRU model acquired previous moment state information in the current state, which assisted in capturing long-term dependencies in the time series and solved the problem of overfitting to some extent. Moreover, the GRU model not only showed good performance in predicting trends, but it was also better than the other models regarding the training time and capturing extreme values, thus making it the most suitable model for predicting the GWL in the Hebei Plain.
- (2)
- Apart from the different principles of each model, the differences in the simulation results can be attributed to factors such as data segmentation during the modeling process, the length of subsequences, and the uncertainty of model parameters. Moreover, the influence of the different activation functions on the GWL in the different models should also be considered. Furthermore, the training frequency of each model in this study was the same, and adaptive improvements should be made for each model in subsequent studies.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Number | Type | Station | City | GWL | Sequence Length (Day) |
---|---|---|---|---|---|
1 | dynamic fluctuations | Huimazhai | Qinhuangdao | 33.83 | 5480 |
2 | Hongmiao | Xingtai | 17.74 | 5480 | |
3 | dynamic increase | Xiliangdian | Baoding | −20.23 | 5480 |
4 | Yanmeidong | Baoding | 1236.14 | 5480 | |
5 | dynamic decrease | Wangduxiancheng | Baoding | −42.33 | 5480 |
6 | XincunIIIzu | Huanghua | −44.21 | 5480 |
Station | Training | Testing | ||||
---|---|---|---|---|---|---|
RMSE | R2 | NSE | RMSE | R2 | NSE | |
Huimazhai | 0.253 | 0.953 | 0.921 | 0.396 | 0.757 | 0.691 |
Hongmiao | 2.299 | 0.98 | 0.967 | 3.823 | 0.867 | 0.804 |
Xiliangdian | 0.298 | 0.995 | 0.994 | 0.511 | 0.915 | 0.908 |
Yanmeidong | 0.204 | 0.998 | 0.909 | 0.193 | 0.998 | 0.984 |
Wangduxiancheng | 0.076 | 0.992 | 0.985 | 0.071 | 0.929 | 0.808 |
XincunIIIzu | 0.052 | 0.999 | 0.998 | 0.045 | 0.990 | 0.940 |
Station | Training | Testing | ||||
---|---|---|---|---|---|---|
RMSE | R2 | NSE | RMSE | R2 | NSE | |
Huimazhai | 0.192 | 0.955 | 0.955 | 0.263 | 0.868 | 0.864 |
Hongmiao | 1.581 | 0.985 | 0.984 | 1.771 | 0.958 | 0.958 |
Xiliangdian | 0.244 | 0.996 | 0.996 | 0.338 | 0.961 | 0.96 |
Yanmeidong | 0.053 | 0.994 | 0.994 | 0.116 | 0.996 | 0.994 |
Wangduxiancheng | 0.049 | 0.994 | 0.994 | 0.036 | 0.953 | 0.95 |
XincunIIIzu | 0.037 | 0.999 | 0.999 | 0.028 | 0.987 | 0.976 |
Station | Training | Testing | ||||
---|---|---|---|---|---|---|
RMSE | R2 | NSE | RMSE | R2 | NSE | |
Huimazhai | 0.201 | 0.959 | 0.95 | 0.128 | 0.979 | 0.968 |
Hongmiao | 1.419 | 0.988 | 0.987 | 0.514 | 0.997 | 0.996 |
Xiliangdian | 0.347 | 0.999 | 0.991 | 0.295 | 0.987 | 0.969 |
Yanmeidong | 0.033 | 0.998 | 0.998 | 0.08 | 0.998 | 0.997 |
Wangduxiancheng | 0.041 | 0.997 | 0.996 | 0.028 | 0.969 | 0.97 |
XincunIIIzu | 0.051 | 0.999 | 0.998 | 0.014 | 0.995 | 0.994 |
Station | Training | Testing | ||||
---|---|---|---|---|---|---|
RMSE | R2 | NSE | RMSE | R2 | NSE | |
Huimazhai | 0.182 | 0.959 | 0.959 | 0.08 | 0.988 | 0.987 |
Hongmiao | 1.449 | 0.987 | 0.987 | 0.518 | 0.996 | 0.996 |
Xiliangdian | 0.229 | 0.996 | 0.996 | 0.123 | 0.995 | 0.995 |
Yanmeidong | 0.04 | 0.998 | 0.996 | 0.098 | 0.998 | 0.996 |
Wangduxiancheng | 0.041 | 0.996 | 0.996 | 0.033 | 0.961 | 0.96 |
XincunIIIzu | 0.081 | 0.999 | 0.996 | 0.027 | 0.995 | 0.978 |
Model | SVM | LSTM | GRU | MLP |
---|---|---|---|---|
Time (min) | 1081 | 1660 | 1251 | 2694 |
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Wu, Z.; Lu, C.; Sun, Q.; Lu, W.; He, X.; Qin, T.; Yan, L.; Wu, C. Predicting Groundwater Level Based on Machine Learning: A Case Study of the Hebei Plain. Water 2023, 15, 823. https://doi.org/10.3390/w15040823
Wu Z, Lu C, Sun Q, Lu W, He X, Qin T, Yan L, Wu C. Predicting Groundwater Level Based on Machine Learning: A Case Study of the Hebei Plain. Water. 2023; 15(4):823. https://doi.org/10.3390/w15040823
Chicago/Turabian StyleWu, Zhenjiang, Chuiyu Lu, Qingyan Sun, Wen Lu, Xin He, Tao Qin, Lingjia Yan, and Chu Wu. 2023. "Predicting Groundwater Level Based on Machine Learning: A Case Study of the Hebei Plain" Water 15, no. 4: 823. https://doi.org/10.3390/w15040823
APA StyleWu, Z., Lu, C., Sun, Q., Lu, W., He, X., Qin, T., Yan, L., & Wu, C. (2023). Predicting Groundwater Level Based on Machine Learning: A Case Study of the Hebei Plain. Water, 15(4), 823. https://doi.org/10.3390/w15040823