Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction
Abstract
:1. Introduction
2. Methodologies
2.1. Benchmark Model
2.1.1. Model Input Selection
2.1.2. Benchmark Model Establishment
2.2. The Improved K-Nearest Neighbors Method
2.3. The Multi-Model Ensemble Method Based on the Improved KNN Algorithm
2.4. Method Evaluation Metric
3. Case Study
3.1. Study Area and Data
3.2. Data Pre-Processing
4. Results and Discussion
4.1. Runoff Prediction Results of Benchmark Model
4.2. Parameters Preferences
4.3. Comparisons of Multi-Model Ensemble Forecasting Methods
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Model | Abbreviation | Model Type |
---|---|---|
Bayesian Ridge Regression | BR | Statistical Model 1 |
Linear Regression | LR | Statistical Model 2 |
Gradient Boosting Decision Tree | GBDT | Machine Learning Model 1 |
Back Propagation Neural Network | BP | Machine Learning Model 2 |
Random Forest | RF | Machine Learning Model 3 |
HistGradient Boosting Regressor | HistG | Machine Learning Model 4 |
Long Short-Term Memory | LSTM | Deep Learning Model 1 |
Gate Recurrent Unit | GRU | Deep Learning Model 2 |
Variable Name | Description | Units |
---|---|---|
PRCP | Precipitation | mm/day |
SRAD | Solar radiation | W/m2 |
Tmax | Maximum temperature | °C |
Tmin | Minimum temperature | °C |
Vp | Vapor pressure | Pa |
Dayl | Day length | s |
Basin Code | Size (km2) | Elevation (m) | Slope (m_km−1) |
---|---|---|---|
01022500 | 620.38 | 92.68 | 17.79 |
12414500 | 2660.37 | 1381.41 | 104.38 |
11532500 | 1590.16 | 725.07 | 112.11 |
12035000 | 768.98 | 149.30 | 29.35 |
Basin Code | 01022500 | 12414500 | 11532500 | 12035000 |
---|---|---|---|---|
Statistics | Training | |||
MAXIMUM (m3/s) | 6790.00 | 33,900.00 | 91,200.00 | 51,800.00 |
MINIMUM (m3/s) | 12.00 | 105.00 | 172.00 | 147.00 |
MEAN (m3/s) | 487.53 | 2239.04 | 3640.09 | 2086.57 |
Standard Deviation (m3/s) | 577.97 | 2692.87 | 6247.83 | 2918.35 |
Coefficient of Variation | 1.19 | 1.20 | 1.72 | 1.40 |
Statistics | Testing | |||
MAXIMUM (m3/s) | 6160.00 | 22,000.00 | 76,900.00 | 34,400.00 |
MINIMUM (m3/s) | 37.00 | 140.00 | 190.00 | 204.00 |
MEAN (m3/s) | 593.45 | 2428.78 | 3462.30 | 2104.60 |
Standard Deviation (m3/s) | 635.49 | 3222.77 | 5741.19 | 2585.62 |
Coefficient of Variation | 1.07 | 1.33 | 1.66 | 1.23 |
Area | Model | NSE | RMSE (m3/s) | MAE (m3/s) |
---|---|---|---|---|
01025000 | BR | 0.32 | 523.1 | 323.0 |
LR | 0.32 | 523.1 | 323.0 | |
GBDT | 0.52 | 441.6 | 258.0 | |
BP | 0.47 | 464.8 | 272.7 | |
RF | 0.52 | 439.9 | 253.3 | |
HistG | 0.55 | 425.8 | 245.5 | |
LSTM | 0.55 | 426.3 | 248.0 | |
GRU | 0.54 | 431.3 | 255.8 | |
average | 0.47 | 459.5 | 272.4 | |
12414500 | BR | 0.38 | 2530.0 | 1576.8 |
LR | 0.38 | 2529.9 | 1577.1 | |
GBDT | 0.53 | 2221.5 | 1228.7 | |
BP | 0.50 | 2274.9 | 1288.0 | |
RF | 0.55 | 2162.8 | 1170.7 | |
HistG | 0.57 | 2122.2 | 1161.1 | |
LSTM | 0.58 | 2095.4 | 1127.4 | |
GRU | 0.58 | 2094.3 | 1120.9 | |
average | 0.51 | 2253.9 | 1281.4 | |
11532500 | BR | 0.67 | 3297.7 | 1866.4 |
LR | 0.67 | 3297.8 | 1866.7 | |
GBDT | 0.78 | 2687.8 | 1536.7 | |
BP | 0.78 | 2689.3 | 1561.4 | |
RF | 0.79 | 2637.0 | 1535.1 | |
HistG | 0.78 | 2718.7 | 1543.3 | |
LSTM | 0.80 | 2557.7 | 1524.3 | |
GRU | 0.79 | 2626.9 | 1534.6 | |
average | 0.76 | 2814.1 | 1621.1 | |
12035000 | BR | 0.70 | 1425.9 | 928.8 |
LR | 0.70 | 1425.9 | 928.9 | |
GBDT | 0.83 | 1069.4 | 669.7 | |
BP | 0.82 | 1096.3 | 723.2 | |
RF | 0.82 | 1098.5 | 674.4 | |
HistG | 0.82 | 1099.9 | 670.8 | |
LSTM | 0.84 | 1034.5 | 653.8 | |
GRU | 0.84 | 1047.2 | 658.7 | |
average | 0.79 | 1162.2 | 738.5 |
Basin Code | Model | NSE | RMSE (m3/s) | MAE (m3/s) |
---|---|---|---|---|
01022500 | IKNN-MME | 0.81 | 274.7 | 137.4 |
KNN-MME | 0.80 | 281.2 | 138.7 | |
OLS | 0.56 | 419.7 | 262.9 | |
HistG | 0.55 | 425.8 | 245.5 | |
LSTM | 0.55 | 426.3 | 248.0 | |
GRU | 0.54 | 431.3 | 255.8 | |
12414500 | IKNN-MME | 0.84 | 1290.2 | 553.6 |
KNN-MME | 0.83 | 1337.3 | 561.6 | |
OLS | 0.56 | 2149.0 | 1262.7 | |
HistG | 0.57 | 2122.2 | 1161.1 | |
LSTM | 0.58 | 2095.4 | 1127.4 | |
GRU | 0.58 | 2094.3 | 1120.9 | |
11532500 | IKNN-MME | 0.89 | 1939.3 | 808.3 |
KNN-MME | 0.88 | 2030.1 | 822.3 | |
OLS | 0.80 | 2583.3 | 1505.2 | |
RF | 0.79 | 2637.0 | 1535.1 | |
LSTM | 0.80 | 2557.7 | 1524.3 | |
GRU | 0.79 | 2626.9 | 1534.6 | |
12035000 | IKNN-MME | 0.89 | 875.8 | 372.0 |
KNN-MME | 0.87 | 925.2 | 382.5 | |
OLS | 0.84 | 1047.9 | 669.4 | |
GBDT | 0.83 | 1069.4 | 669.7 | |
LSTM | 0.84 | 1034.5 | 653.8 | |
GRU | 0.84 | 1047.2 | 658.7 |
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Xie, T.; Chen, L.; Yi, B.; Li, S.; Leng, Z.; Gan, X.; Mei, Z. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction. Water 2024, 16, 69. https://doi.org/10.3390/w16010069
Xie T, Chen L, Yi B, Li S, Leng Z, Gan X, Mei Z. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction. Water. 2024; 16(1):69. https://doi.org/10.3390/w16010069
Chicago/Turabian StyleXie, Tao, Lu Chen, Bin Yi, Siming Li, Zhiyuan Leng, Xiaoxue Gan, and Ziyi Mei. 2024. "Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction" Water 16, no. 1: 69. https://doi.org/10.3390/w16010069
APA StyleXie, T., Chen, L., Yi, B., Li, S., Leng, Z., Gan, X., & Mei, Z. (2024). Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction. Water, 16(1), 69. https://doi.org/10.3390/w16010069