Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling
Abstract
:1. Introduction
2. Integrating ANNs with the VIC Model
2.1. Variable Infiltration Capacity Model
2.2. Artificial Neural Networks
2.3. Hybrid VIC and ANN Model
3. Model Application and Case Study
3.1. Study Area
3.2. Data Preparation
3.3. Calibrating the VIC Model
3.4. Configuring the ABCM
- Calculate , …, and for Subbasin 1; , …, and for Subbasin 2; , , and for Subbasin 3.
- Let Ei(t) plus be the corrected flow of each subbasin.
- Determine the optimal number of hidden neurons of each subbasin’s ABCM by trial-and-error.
3.5. Configuring the ARM
4. Results and Discussion
5. Summary and Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Model | Catchment | Calibration | Validation | ||
---|---|---|---|---|---|
NSE | RE | NSE | RE | ||
(%) | (%) | (%) | (%) | ||
VIC Model | Subbasin 1 | 86.63 | 8.73 | 85.39 | 1.34 |
Subbasin 2 | 78.46 | −18.61 | 78.23 | −20.3 | |
Subbasin 3 | 78.82 | −16.07 | 77.44 | −16.57 |
Catchment | Calibration | Validation | ||
---|---|---|---|---|
NSE | RE | NSE | RE | |
(%) | (%) | (%) | (%) | |
Subbasin 1 | 99.76 (99.68–99.75) | 0.09 (−0.04–0.11) | 99.72 (99.65–99.73) | 0.47 (0.08–0.62) |
Subbasin 2 | 97.92 (96.81–98.24) | 1.73 (1.35–2.23) | 97.63 (96.19–98.87) | 1.94 (1.25–3.12) |
Subbasin 3 | 97.81 (96.29–98.23) | 1.85 (1.07–2.14) | 97.19 (95.82–96.64) | 2.13 (2.34–3.97) |
Model | Calibration | Validation | ||
---|---|---|---|---|
NSE | RE | NSE | RE | |
(%) | (%) | (%) | (%) | |
VIC | 78.03 | −21.51 | 78.47 | −20.02 |
VIC and MC | 95.77 | −1.53 | 95.34 | −0.85 |
VIC and Regression | 95.62 | −1.74 | 95.11 | −1.2 |
VIC and ANN | 98.89 (98.24–98.92) | 0.43 (0.24–0.46) | 97.76 (96.73–97.85) | −0.86 (−1.11–0.14) |
Model Phase | Category | POC (%) | Prediction Band with Respect to Ensemble Mean (%) | No. of Patterns | % of Total Data |
---|---|---|---|---|---|
Calibration | Complete flow | 64.45 | 8.74 | 1826 | 100 |
Low flow | 58.98 | 6.83 | 1213 | 66.40 | |
Medium flow | 72.36 | 11.43 | 515 | 28.20 | |
High flow | 90.63 | 12.03 | 98 | 5.39 | |
Validation | Complete flow | 66.92 | 10.89 | 1096 | 100 |
Low flow | 59.15 | 8.44 | 675 | 61.62 | |
Medium flow | 76.95 | 12.08 | 338 | 30.86 | |
High flow | 89.49 | 13.07 | 83 | 7.52 |
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Meng, C.; Zhou, J.; Tayyab, M.; Zhu, S.; Zhang, H. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling. Water 2016, 8, 407. https://doi.org/10.3390/w8090407
Meng C, Zhou J, Tayyab M, Zhu S, Zhang H. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling. Water. 2016; 8(9):407. https://doi.org/10.3390/w8090407
Chicago/Turabian StyleMeng, Changqing, Jianzhong Zhou, Muhammad Tayyab, Shuang Zhu, and Hairong Zhang. 2016. "Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling" Water 8, no. 9: 407. https://doi.org/10.3390/w8090407
APA StyleMeng, C., Zhou, J., Tayyab, M., Zhu, S., & Zhang, H. (2016). Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling. Water, 8(9), 407. https://doi.org/10.3390/w8090407