Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models
Abstract
1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
2.2.1. Forcing Data
2.2.2. Static Data
3. Methodology
3.1. BTOP Model
3.2. Long Short-Term Memory Network
3.3. Experimental Design
3.3.1. Pre-Experiment of LSTM
3.3.2. Basin-Based LSTM and Gauge-Based LSTM
3.3.3. Transfer-Based LSTM
3.4. Model Evaluation Criteria
4. Results and Discussion
4.1. Hyperparameters of LSTM
4.2. Performance of BTOP Model
4.3. Performance of B-LSTM and G-LSTM Models
4.4. Performance of T-LSTM Model
4.5. Discussion about GZ Station
- Scheme a: The model was trained only with data from GB and BHQ stations and then transferred to the GZ station (Case ②-a).
- Scheme b: The model was trained only with data from Lhasa station and then transferred to the GZ station (Case ②-b).
4.6. Limitations and Future Research Directions
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Abbreviation | Case | Source Station | Training Periods | Validation Period | Target Station | Testing Periods |
---|---|---|---|---|---|---|
T-LSTM | ① | GB, BHQ, GZ | 2010–2013 | 2014–2015 | Lhasa | 2010–2015 |
② | GB, BHQ, Lhasa | 2010–2013 | 2014–2015 | GZ | 2010–2015 | |
③ | GB, GZ, Lhasa | 2010–2013 | 2014–2015 | BHQ | 2010–2015 | |
④ | BHQ, GZ, Lhasa | 2010–2013 | 2014–2015 | GB | 2010–2015 |
Model | Batch Size | Window Size | Hidden Layer Units | Gradient Descent Method | Learning Rate | |
---|---|---|---|---|---|---|
Pretraining | Tested | 2, 4, 8, 16, 32, 64, 128, 256, 512 | 10, 20, 30, 40, 50, 60, 70, 80, 90, 120, 150, 180 | 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 | RMSprop Adam | 0.001, 0.002, 0.003, 0.004, 0.005, 0.008, 0.01, 0.015, 0.02, 0.03, 0.05 |
G-LSTM B-LSTM | Tested | 32, 64 | 30, 60, 90 | 40, 60, 80 | RMSprop Adam | 0.001, 0.002, 0.003 |
Selected | 32 | 30 | 40 | RMSprop | 0.002 | |
T-LSTM | Tested | 32, 64 | 30, 60, 90 | 40, 60, 80 | RMSprop Adam | 0.001, 0.002, 0.003 |
Selected | 64 | 60 | 60 | Adam | 0.003 |
Station | Model | Testing Period | NSE | KGE | RBias (%) |
---|---|---|---|---|---|
Lhasa | BTOP | 2014–2015 | 0.58 | 0.4 | −29.49 |
BTOP | 2014 | 0.62 | 0.41 | −28.58 | |
B-LSTM | 2014 | 0.76 | 0.80 | 0.31 | |
B-LSTM | 2014–2015 | 0.62 | 0.75 | 12.8 | |
GB | BTOP | 2014–2015 | 0.79 | 0.72 | −14.89 |
B-LSTM | 2014–2015 | 0.84 | 0.82 | 1.53 | |
G-LSTM | 2014–2015 | 0.84 | 0.86 | 0 | |
BHQ | BTOP | 2014–2015 | 0.49 | 0.56 | 32.34 |
B-LSTM | 2014–2015 | 0.78 | 0.87 | 3.26 | |
G-LSTM | 2014–2015 | 0.85 | 0.89 | 0.09 | |
GZ | BTOP | 2014–2015 | 0.34 | 0.19 | −55.07 |
B-LSTM | 2014–2015 | 0.95 | 0.97 | −0.76 | |
G-LSTM | 2014–2015 | 0.93 | 0.86 | −0.12 |
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Yue, J.; Zhou, L.; Du, J.; Zhou, C.; Nimai, S.; Wu, L.; Ao, T. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models. Water 2024, 16, 2161. https://doi.org/10.3390/w16152161
Yue J, Zhou L, Du J, Zhou C, Nimai S, Wu L, Ao T. Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models. Water. 2024; 16(15):2161. https://doi.org/10.3390/w16152161
Chicago/Turabian StyleYue, Jiajia, Li Zhou, Juan Du, Chun Zhou, Silang Nimai, Lingling Wu, and Tianqi Ao. 2024. "Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models" Water 16, no. 15: 2161. https://doi.org/10.3390/w16152161
APA StyleYue, J., Zhou, L., Du, J., Zhou, C., Nimai, S., Wu, L., & Ao, T. (2024). Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models. Water, 16(15), 2161. https://doi.org/10.3390/w16152161