Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data
3. Methodology
3.1. Evaluation Criteria
3.2. Hydrological Model: The Block-Wise Use of TOPMODEL (BTOP Model)
3.3. Long Short-Term Memory Network (LSTM)
3.4. Feature Dimensionality Reduction Method
3.4.1. Pearson Correlation Coefficient (PCC)
3.4.2. Principal Component Analysis (PCA)
3.5. Model Design
3.5.1. Standalone LSTM Settings
3.5.2. Construction of Hybrid Models
- Hybrid-1: Pearson correlation analysis, a common feature selection method, was employed to screen the feature variables demonstrating high correlation with the observed discharge as additional input to the standalone LSTM.
- Hybrid-2: Employing principal component analysis as a feature dimensionality reduction method, converted eight input features into a few principal components and fed them as additional features into the standalone LSTM.
- Hybrid-3: All output variables from the BTOP model were directly fed into the standalone LSTM as additional features without any dimensionality reduction.
- The hybrid model adopted the same model structure as the standalone LSTM (avoiding overfitting and underfitting), with variations in input variables only. The hybrid models were executed ten times on each sub-basin, and the average value was taken.
4. Results and Discussion
4.1. BTOP Model Simulation
4.2. Feature Dimensionality Reduction Results
4.2.1. Feature Selection (PCC)
4.2.2. Feature Extraction (PCA)
4.3. Comparison of Performance between Varied Models
5. Conclusions
- (1)
- The data-driven LSTM demonstrated superior and more stable simulation performance compared to the process-based BTOP model. The BTOP model failed to simulate two downstream basins with NSE of only 0.15 and 0.17. On the contrary, LSTM performed well across the basin, with NSE values all exceeding 0.70. Moreover, the BTOP model significantly underestimated the discharge of the six sub-basins, while the flow duration curve simulated by LSTM fitted well with that of the observed discharge.
- (2)
- Feeding the output variables from the BTOP model into LSTM as input features could provide LSTM with more physical information for learning, thereby improving simulation accuracy. Hybrid-1 and Hybrid-2 displayed comparable performances throughout the basin, both outperforming the standalone LSTM. Moreover, the exceedance probability shows that the fit of the flow duration curve of Hybrid-1 and Hybrid-2 was better than that of the standalone LSTM, and the main error of the two hybrid models originates from the slight underestimation of high flow.
- (3)
- The feature selection method, PCC, and the feature extraction method, PCA, can effectively eliminate noise within the hydrological sequences. Feeding all the BTOP model estimates into LSTM (Hybrid-3) does not enhance simulation performance but instead leads to poor simulation accuracy due to redundant and irrelevant information. Notably, Hybrid-3 exhibited overestimation behavior in the mid-flow, resulting in model accuracy far lower than Hybrid-1 and Hybrid-2 and even lower than the standalone LSTM. It is confirmed that implementing the feature dimension reduction method before constructing the hybrid model is an effective strategy to improve simulation accuracy.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Unit | Description |
---|---|---|
Effec.P | mm | Net precipitation |
ET | mm | Actual evaporation |
ET0 | mm | Actual interception evapotranspiration |
SD | mm | The soil moisture saturation deficit |
Srz | mm | The root zone water storage |
Suz | mm | The unsaturated zone water storage |
Qoft | mm | Flow generation flux, the sum of simulated Hortonian overland flow flux, saturation excess runoff flux and groundwater flux |
Qv | mm | Groundwater recharge flux |
Sub- Basin | Calibration Period | Validation Period | ||||||
---|---|---|---|---|---|---|---|---|
MAE (m3/s) | RMSE (m3/s) | NSE | PCC | MAE (m3/s) | RMSE (m3/s) | NSE | PCC | |
SRB-1 | 19.62 | 37.54 | 0.77 | 0.89 | 19.01 | 31.19 | 0.63 | 0.82 |
SRB-2 | 37.15 | 64.72 | 0.56 | 0.81 | 33.79 | 53.07 | 0.66 | 0.88 |
SRB-3 | 63.79 | 103.09 | 0.70 | 0.89 | 62.34 | 94.72 | 0.66 | 0.89 |
SRB-4 | 125.31 | 155.40 | 0.65 | 0.85 | 125.34 | 155.81 | 0.55 | 0.82 |
SRB-5 | 205.73 | 324.06 | 0.14 | 0.54 | 199.18 | 340.60 | 0.15 | 0.59 |
SRB-6 | 210.34 | 338.52 | 0.19 | 0.54 | 192.25 | 333.84 | 0.17 | 0.59 |
Sub-Basin | Lag Time (day) | Effec.P | ET | ET0 | SD | Srz | Suz | Qoft | Qv |
---|---|---|---|---|---|---|---|---|---|
SRB-1 | 0 | 0.43 ** | 0.19 ** | 0.19 ** | −0.58 ** | 0.17 ** | 0.43 ** | 0.71 ** | 0.60 ** |
1 | 0.69 ** | 0.18 ** | 0.28 ** | −0.58 ** | 0.17 ** | 0.47 ** | 0.78 ** | 0.80 ** | |
2 | 0.37 ** | 0.16 ** | 0.20 ** | −0.38 ** | 0.11 ** | 0.34 ** | 0.37 ** | 0.39 ** | |
SRB-2 | 0 | 0.41 ** | 0.35 ** | 0.24 ** | −0.68 ** | 0.05 * | −0.11 * | 0.72 ** | 0.54 ** |
1 | 0.55 ** | 0.35 ** | 0.30 ** | −0.67 ** | 0.04 * | 0.11 ** | 0.76 ** | 0.67 ** | |
2 | 0.35 ** | 0.22 ** | 0.33 ** | −0.54 ** | 0.01 | −0.12 ** | 0.51 ** | 0.42 ** | |
SRB-3 | 0 | 0.36 ** | 0.28 ** | 0.20 ** | −0.64 ** | 0.14 ** | 0.04 | 0.67 ** | 0.51 ** |
1 | 0. 69 ** | 0.28 ** | 0.33 ** | −0.67 ** | 0.14 ** | 0.04 * | 0.86 ** | 0.82 ** | |
2 | 0.41 ** | 0.26 ** | 0.25 ** | −0.49 ** | 0.09 ** | 0.02 | 0.45 ** | 0.46 ** | |
SRB-4 | 0 | 0.24 ** | 0.22 ** | 0.17 ** | −0.55 ** | 0.15 ** | 0.03 | 0.57 ** | 0.41 ** |
1 | 0.66 ** | 0.22 ** | 0.31 ** | −0.62 ** | 0.16 ** | 0.04 | 0.85 ** | 0.78 ** | |
2 | 0.43 ** | 0.20 ** | 0.26 ** | −0.45 ** | 0.11 ** | 0.01 | 0.55 ** | 0.50 ** | |
SRB-5 | 0 | 0.20 ** | 0.18 ** | 0.13 ** | −0.23 ** | 0.16 ** | −0.01 | 0.36 ** | 0.31 ** |
1 | 0.51 ** | 0.17 ** | 0.25 ** | −0.25 ** | 0.16 ** | −0.01 | 0.56 ** | 0.56 ** | |
2 | 0.30 ** | 0.15 ** | 0.20 ** | −0.15 ** | 0.12 ** | −0.04 | 0.32 ** | 0.32 ** | |
SRB-6 | 0 | 0.19 ** | 0.12 ** | 0.11 ** | −0.25 ** | 0.19 ** | 0.02 | 0.36 ** | 0.30 ** |
1 | 0.50 ** | 0.11 ** | 0.23 ** | −0.27 ** | 0.19 ** | 0.03 | 0.56 ** | 0.56 * | |
2 | 0.30 ** | 0.09 ** | 0.18 ** | −0.18 ** | 0.15 ** | −0.01 | 0.34 ** | 0.33 ** |
SRB-1 | SRB-2 | SRB-3 | ||||
Ingredient | Eigenvalues | Cumulative (%) | Eigenvalues | Cumulative (%) | Eigenvalues | Cumulative (%) |
1 | 4.207 | 46.740 | 4.146 | 46.063 | 4.059 | 45.102 |
2 | 1.395 | 62.240 | 1.534 | 63.107 | 1.485 | 61.598 |
3 | 1.138 | 74.882 | 1.102 | 75.353 | 1.122 | 74.068 |
4 | 1.002 | 83.873 | 1.001 | 86.445 | 1.003 | 85.143 |
5 | 0.560 | 91.172 | 0.596 | 93.102 | 0.621 | 92.048 |
6 | 0.297 | 94.773 | 0.378 | 97.303 | 0.407 | 96.635 |
7 | 0.227 | 98.069 | 0.169 | 99.178 | 0.190 | 98.743 |
8 | 0.130 | 99.516 | 0.049 | 99.720 | 0.077 | 99.604 |
9 | 0.044 | 100.000 | 0.025 | 100.000 | 0.036 | 100.000 |
SRB-4 | SRB-5 | SRB-6 | ||||
Ingredient | Eigenvalues | Cumulative (%) | Eigenvalues | Cumulative (%) | Eigenvalues | Cumulative (%) |
1 | 3.918 | 43.537 | 3.413 | 42.368 | 3.487 | 42.078 |
2 | 1.592 | 61.222 | 1.955 | 64.086 | 2.005 | 64.352 |
3 | 1.139 | 73.879 | 1.672 | 75.085 | 1.554 | 75.361 |
4 | 1.007 | 84.885 | 1.004 | 85.857 | 1.072 | 85.897 |
5 | 0.605 | 91.798 | 0.416 | 92.239 | 0.381 | 92.285 |
6 | 0.395 | 96.184 | 0.236 | 95.905 | 0.235 | 95.916 |
7 | 0.224 | 98.674 | 0.172 | 98.531 | 0.133 | 98.528 |
8 | 0.084 | 99.603 | 0.092 | 99.549 | 0.092 | 99.546 |
9 | 0.036 | 100.000 | 0.041 | 100.000 | 0.041 | 100.000 |
Sub-Basin Number | Metrics | BTOP | Standalone LSTM | Hybrid-1 | Hybrid-2 | Hybrid-3 |
---|---|---|---|---|---|---|
SRB-1 | MAE (m3/s) | 18.95 | 10.50 | 11.96 | 10.50 | 15.06 |
RMSE (m3/s) | 31.43 | 26.92 | 25.95 | 25.01 | 31.78 | |
NSE | 0.63 | 0.73 | 0.75 | 0.76 | 0.64 | |
PCC | 0.83 | 0.86 | 0.88 | 0.89 | 0.85 | |
SRB-2 | MAE (m3/s) | 34.59 | 18.80 | 18.38 | 19.17 | 26.01 |
RMSE (m3/s) | 53.82 | 46.02 | 40.80 | 41.62 | 45.52 | |
NSE | 0.66 | 0.75 | 0.80 | 0.80 | 0.76 | |
PCC | 0.88 | 0.87 | 0.90 | 0.90 | 0.89 | |
SRB-3 | MAE (m3/s) | 63.29 | 32.75 | 29.75 | 36.74 | 36.23 |
RMSE (m3/s) | 95.95 | 68.19 | 61.84 | 61.07 | 74.19 | |
NSE | 0.66 | 0.83 | 0.86 | 0.86 | 0.80 | |
PCC | 0.90 | 0.92 | 0.93 | 0.94 | 0.90 | |
SRB-4 | MAE (m3/s) | 126.09 | 48.51 | 37.82 | 44.17 | 54.02 |
RMSE (m3/s) | 157.14 | 77.96 | 69.60 | 72.58 | 84.39 | |
NSE | 0.55 | 0.89 | 0.91 | 0.90 | 0.87 | |
PCC | 0.81 | 0.95 | 0.96 | 0.95 | 0.94 | |
SRB-5 | MAE (m3/s) | 203.60 | 71.50 | 67.43 | 73.54 | 103.70 |
RMSE (m3/s) | 345.39 | 157.43 | 152.89 | 151.74 | 165.59 | |
NSE | 0.15 | 0.82 | 0.83 | 0.84 | 0.80 | |
PCC | 0.62 | 0.92 | 0.92 | 0.92 | 0.91 | |
SRB-6 | MAE (m3/s) | 196.34 | 81.87 | 75.66 | 81.41 | 112.22 |
RMSE (m3/s) | 338.47 | 184.85 | 167.10 | 168.24 | 196.35 | |
NSE | 0.17 | 0.75 | 0.80 | 0.79 | 0.72 | |
PCC | 0.62 | 0.88 | 0.90 | 0.90 | 0.88 |
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Nimai, S.; Ren, Y.; Ao, T.; Zhou, L.; Liang, H.; Cui, Y. Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin. Water 2023, 15, 3758. https://doi.org/10.3390/w15213758
Nimai S, Ren Y, Ao T, Zhou L, Liang H, Cui Y. Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin. Water. 2023; 15(21):3758. https://doi.org/10.3390/w15213758
Chicago/Turabian StyleNimai, Silang, Yufeng Ren, Tianqi Ao, Li Zhou, Hanxu Liang, and Yanmin Cui. 2023. "Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin" Water 15, no. 21: 3758. https://doi.org/10.3390/w15213758