Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction
Abstract
:1. Introduction
2. Materials and Methods
2.1. Model
2.1.1. Long Short-Term Memory Network
2.1.2. Benchmark Model
2.2. Study Area and Data
2.3. Experimental Setup
2.3.1. Experiment 1: Combination of Input Data for Learning
2.3.2. Experiment 2: Multi-Basins Integrated Learning
2.4. Model Evaluation
3. Results and Discussion
3.1. Best Combination of Input Variables for LSTM Learning
3.2. One LSTM for Predicting Streamflow in Each Basin
3.3. Performance Evaluation for Flow Segments
3.4. Integrated Learning Considering a Basin Characteristic
4. Conclusions
- The performance and robustness of the outputs from LSTM can be enhanced by using various meteorological information as an input variable of LSTM;
- The LSTM could reasonably predict streamflow in the basins through the integrated learning method. This result means that the integrated learning method is a possible approach for reducing the data demand, and the concept of regionalization can be applied to LSTM. This regionalization approach may also help the streamflow in ungauged basins through further research;
- In particular, at least in the basins selected in this study, low-flow predictions are improved through the integrated learning;
- The selection of target basins for the integrated learning affects the performance of LSTM. Therefore, further research is needed on this topic.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Basin Number | Basin Name | Area (km2) | Annual Mean Precipitation, P (mm/year) | Annual Mean Streamflow, Q (mm/year) | Runoff Ratio | Curve Number |
---|---|---|---|---|---|---|
1 | Chungju | 6661.5 | 1305.7 | 742.0 | 0.57 | 64.2 |
2 | Soyanggang | 2694.3 | 1276.1 | 803.5 | 0.63 | 53.8 |
3 | Namgang | 2281.7 | 1519.8 | 1027.1 | 0.68 | 65.2 |
4 | Andong | 1590.7 | 1178.0 | 606.0 | 0.51 | 61.4 |
5 | Imha | 1367.7 | 1115.5 | 466.9 | 0.42 | 67.8 |
6 | Yongdam | 930.4 | 1446.6 | 815.9 | 0.56 | 64.3 |
7 | Hapcheon | 928.9 | 1329.0 | 712.5 | 0.54 | 59.5 |
8 | Seomjingang | 763.5 | 1388.2 | 785.6 | 0.57 | 69.6 |
9 | Goesan | 676.7 | 1294.4 | 651.1 | 0.50 | 68.7 |
10 | Woonmoon | 301.9 | 1149.1 | 705.5 | 0.61 | 68.6 |
11 | Hoengseong | 207.9 | 1335.0 | 777.7 | 0.58 | 54.1 |
12 | Boryeong | 162.3 | 1160.5 | 770.3 | 0.66 | 59.1 |
13 | Gwangdong | 120.7 | 1311.2 | 721.4 | 0.55 | 70.1 |
Segment | Magnitude of Flow | Range of Percentile |
---|---|---|
Q1 | Highest flows | 0 to 0.25 |
Q2 | Higher flows | 0.25 to 0.5 |
Q3 | Lower flows | 0.5 to 0.75 |
Q4 | Lowest flows | 0.75 to 1 |
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Choi, J.; Won, J.; Jang, S.; Kim, S. Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction. Water 2022, 14, 2910. https://doi.org/10.3390/w14182910
Choi J, Won J, Jang S, Kim S. Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction. Water. 2022; 14(18):2910. https://doi.org/10.3390/w14182910
Chicago/Turabian StyleChoi, Jeonghyeon, Jeongeun Won, Suhyung Jang, and Sangdan Kim. 2022. "Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction" Water 14, no. 18: 2910. https://doi.org/10.3390/w14182910
APA StyleChoi, J., Won, J., Jang, S., & Kim, S. (2022). Learning Enhancement Method of Long Short-Term Memory Network and Its Applicability in Hydrological Time Series Prediction. Water, 14(18), 2910. https://doi.org/10.3390/w14182910