Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis
Abstract
:1. Introduction
2. Study Area and Data Processing
2.1. Study Area
2.2. Data Processing
3. Methodology
3.1. Model Introduction
3.2. Wavelet Analysis
3.3. Evaluation Indicators
3.4. Interpretable Machine Learning Method
4. Results
4.1. Feature Selection
4.1.1. Selection of Atmospheric Circulation Factors
4.1.2. Delayed Effect Analysis
4.2. Driving Effect of Atmospheric Circulation Factors on Runoff Change
4.3. Comparative Analysis of Model Prediction Performance
4.3.1. Prediction Performance of Different Models in the Same Forecast Period
4.3.2. Prediction Performance of Optimal Model under Different Foresight Periods
4.4. Interpretability Analysis of LSTM Model
5. Discussion
5.1. Reasons for Differences in Model Prediction Accuracy
5.2. Uncertainty
5.3. Advantages and Limitations
6. Conclusions
- (1)
- The NPI is the most influential atmospheric circulation factor affecting the runoff in the XJB.
- (2)
- When comparing different models with the same forecast period, the LSTM model had higher NSE results in the QJ, LZ, GG, and WZ, with values of 0.950, 0.960, 0.954, and 0.955, respectively. These values were higher than those in the other three models tested at the same stations. Therefore, it can be concluded that the LSTM model is the optimal choice among the four models used in this study.
- (3)
- With the optimal model, the LSTM model, its prediction results decreased as the foresight period increased. Specifically, the NSE decreased by 4.7% when the foresight period increased from one month to two months, and it decreased by 3.9% when the foresight period increased from two months to three months. This suggested that although the decrease in the NSE was slow as the foresight period increased, there was a converging trend of a declining NSE with a longer foresight period.
- (4)
- Based on SHAP values, an interpretability analysis was conducted on the LSTM model. The results showed that in the XJB, historical runoff had the greatest impact on runoff prediction results, followed by precipitation, evaporation, and the NPI. Evaporation was negatively correlated with runoff, while historical runoff, precipitation, and the NPI were positively correlated.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Evaluation Indicators | Formula | Optimal Value |
---|---|---|
RMSE | 0 | |
MAE | 0 | |
NSE | 1 |
Hydrological Station | ENSO | PDO | NAO | AO | AMO | DMI | NPI | PNA | SSI |
---|---|---|---|---|---|---|---|---|---|
QJ | 0.026 | 0.013 | −0.029 | 0.073 | −0.032 | −0.015 | 0.512 ** | −0.052 | 0.018 |
LZ | 0.072 | 0.055 | −0.049 | 0.087 * | 0.026 | 0.024 | 0.452 ** | −0.088 * | 0.034 |
GG | −0.021 | −0.063 | −0.042 | 0.064 | −0.028 | −0.026 | 0.487 ** | −0.056 | 0.008 |
WZ | 0.040 | 0.024 | −0.001 | 0.098 ** | −0.031 | 0.006 | 0.517 ** | −0.087 * | 0.021 |
Station | Model | Training Set | Test Set | ||||
---|---|---|---|---|---|---|---|
NSE | RMSE (103 m3/s) | MAE (103 m3/s) | NSE | RMSE (103 m3/s) | MAE (103 m3/s) | ||
QJ | LSTM | 0.944 | 0.456 | 0.274 | 0.950 | 0.249 | 0.203 |
CNN-LSTM | 0.921 | 0.529 | 0.308 | 0.920 | 0.305 | 0.244 | |
Conv-LSTM | 0.920 | 0.535 | 0.312 | 0.939 | 0.275 | 0.218 | |
Bi-LSTM | 0.927 | 0.526 | 0.296 | 0.942 | 0.269 | 0.212 | |
LZ | LSTM | 0.959 | 0.252 | 0.137 | 0.960 | 0.241 | 0.210 |
CNN-LSTM | 0.925 | 0.338 | 0.163 | 0.926 | 0.343 | 0.282 | |
Conv-LSTM | 0.929 | 0.337 | 0.231 | 0.925 | 0.363 | 0.326 | |
Bi-LSTM | 0.916 | 0.362 | 0.251 | 0.926 | 0.340 | 0.269 | |
GG | LSTM | 0.933 | 0.405 | 0.237 | 0.954 | 0.221 | 0.195 |
CNN-LSTM | 0.922 | 0.416 | 0.248 | 0.923 | 0.286 | 0.242 | |
Conv-LSTM | 0.927 | 0.415 | 0.246 | 0.919 | 0.296 | 0.249 | |
Bi-LSTM | 0.928 | 0.412 | 0.242 | 0.922 | 0.288 | 0.245 | |
WZ | LSTM | 0.950 | 1.318 | 0.869 | 0.955 | 0.833 | 0.698 |
CNN-LSTM | 0.934 | 1.439 | 0.901 | 0.923 | 1.060 | 0.818 | |
Conv-LSTM | 0.900 | 1.695 | 0.928 | 0.906 | 1.197 | 0.922 | |
Bi-LSTM | 0.920 | 1.460 | 0.916 | 0.911 | 1.153 | 0.867 |
Forecast Period | Error Indicator | QJ | LZ | GG | WZ |
---|---|---|---|---|---|
1 month | NSE | 0.950 | 0.960 | 0.954 | 0.955 |
RMSE (103 m3/s) | 0.249 | 0.241 | 0.221 | 0.833 | |
MAE (103 m3/s) | 0.203 | 0.210 | 0.195 | 0.698 | |
2 month | NSE | 0.897 | 0.901 | 0.889 | 0.893 |
RMSE (103 m3/s) | 0.295 | 0.274 | 0.243 | 1.310 | |
MAE (103 m3/s) | 0.266 | 0.280 | 0.244 | 0.832 | |
3 month | NSE | 0.858 | 0.863 | 0.859 | 0.849 |
RMSE (103 m3/s) | 0.312 | 0.290 | 0.269 | 1.664 | |
MAE (103 m3/s) | 0.297 | 0.321 | 0.276 | 0.920 |
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Tian, Q.; Gao, H.; Tian, Y.; Jiang, Y.; Li, Z.; Guo, L. Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis. Water 2023, 15, 3184. https://doi.org/10.3390/w15183184
Tian Q, Gao H, Tian Y, Jiang Y, Li Z, Guo L. Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis. Water. 2023; 15(18):3184. https://doi.org/10.3390/w15183184
Chicago/Turabian StyleTian, Qingqing, Hang Gao, Yu Tian, Yunzhong Jiang, Zexuan Li, and Lei Guo. 2023. "Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis" Water 15, no. 18: 3184. https://doi.org/10.3390/w15183184