Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models
Abstract
:1. Introduction
2. Methods
2.1. Study Area and Data
2.2. Variable Selection
2.2.1. Pearson’s Correlation Coefficient
2.2.2. Stepwise Regression
2.2.3. Copula Entropy
2.3. Prediction Models
2.3.1. Long Short-Term Memory (LSTM)
2.3.2. Gate Recurrent Unit (GRU)
2.3.3. Gradient Boosted Decision Tree (GBDT)
2.3.4. Random Forest (RF)
2.3.5. Support Vector Regression (SVR)
2.4. Metrics of Performance Evaluation
2.5. Model Calculation Scheme
3. Results
3.1. Variable Selection
3.2. Model Structure and Parameter Selection
3.3. Comparison of Various Models’ Performance
3.4. Accuracy of Peak Flow and Low Flow Forecasts
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Station | Stepwise Regression | Copula Entropy | ||
---|---|---|---|---|
Variables | Lag (Month) | Variables | Lag (Month) | |
Gaochang | Average Temperature | 7 | Maximum Temperature | 1 |
Runoff | 12 | East Asian Trough Intensity Index | 6 | |
Northern Hemisphere Polar Vortex Central Intensity Index | 1 | Average Temperature | 7 | |
Maximum Temperature | 6 | Daylight Hours | 2 | |
North American Subtropical High Area Index | 12 | Maximum Temperature | 7 | |
Runoff | 1 | Runoff | 6 | |
Relative Humidity | 1 | Daylight Hours | 1 | |
Tibet Plateau Region 1 Index | 5 | East Asian Trough Intensity Index | 12 | |
Asia Polar Vortex Area Index | 1 | Average Temperature | 1 | |
Indian Ocean Warm Pool Strength Index | 9 | Runoff | 12 | |
Cuntan | Maximum Temperature | 7 | Maximum Temperature | 7 |
Runoff | 12 | Maximum Temperature | 1 | |
Northern Hemisphere Polar Vortex Intensity Index | 2 | Average Temperature | 7 | |
Runoff | 1 | East Asian Trough Intensity Index | 7 | |
North American Subtropical High Intensity Index | 12 | Runoff | 6 | |
Atlantic-European Polar Vortex Intensity Index | 7 | Average Temperature | 1 | |
Daylight Hours | 12 | Runoff | 12 | |
Asia Polar Vortex Intensity Index | 6 | East Asian Trough Intensity Index | 1 | |
Eurasian Zonal Circulation Index | 9 | Daylight Hours | 8 | |
Air Pressure | 3 | Daylight Hours | 2 |
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Li, X.; Zhang, L.; Zeng, S.; Tang, Z.; Liu, L.; Zhang, Q.; Tang, Z.; Hua, X. Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models. Sustainability 2022, 14, 11149. https://doi.org/10.3390/su141811149
Li X, Zhang L, Zeng S, Tang Z, Liu L, Zhang Q, Tang Z, Hua X. Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models. Sustainability. 2022; 14(18):11149. https://doi.org/10.3390/su141811149
Chicago/Turabian StyleLi, Xiao, Liping Zhang, Sidong Zeng, Zhenyu Tang, Lina Liu, Qin Zhang, Zhengyang Tang, and Xiaojun Hua. 2022. "Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models" Sustainability 14, no. 18: 11149. https://doi.org/10.3390/su141811149