Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan
2. Materials and Methods
2.1. Study Area
2.2. The Hydrologic Modeling System (HEC-HMS)
2.2.1. Basin Model
2.2.2. Time-Series Data and Meteorologic Model
2.2.3. Model Parameters Optimization
2.3. Support Vector Regression
2.4. Model Performance Criterion
3.1. The HEC-HMS Parameters Calibration and Validation Results
3.2. The SVR Model features Selection Results
3.3. Comparison among Model Validation and Test Results
4. Discussion and Conclusions
- The validation results showed that Type 2 HEC-HMS performed better than Type 1 HEC-HMS.
- The model whose parameter values were derived from multiple typhoon events performed better than the model whose parameter values were derived from only one typhoon event. The model performance was not always proportional to the number of rainfall events for the parameter calibration process, even with the excellent performance of the HEC-HMS during the calibration phase, which might indicate that a global optimization strategy is needed to improve the model validation performance.
- As for the SVR trained models, Type 2 SVR was slightly better than Type 1 SVR; the rainfall pattern of the validation events being typhoons might be the reason.
- Furthermore, even within a concise time range of model training, for instance, only one typhoon event, the SVR model could estimate an excellent rainfall-runoff relationship.
- As for the features selection of the SVR model, when the discharge data of the previous 2-time steps to the target were applied, the model validation results were generally better than the other features selection. The rainfall data features of the previous time step were also proportional to the model validation performance, but not as much as the discharge features were.
- It could be seen that Type 2 SVR within one typhoon event for model training performed better than Type 1 SVR, which utilized one-year rainfall and discharge data for training. The possible reason might be that the rainfall pattern, which was a typhoon, of the validation event was the same as in Type 2 SVR model training. Other rainfall patterns for model training might give different results. This indicates that data quality is essential for SVR model training.
- The quality of observation data is essential to using the HEC-HMS and the SVR model. However, the HEC-HMS would need the extra effort of field data collection to determine the geographically related parameters such as land use and soil type; additional parameter optimization efforts would also be needed. While SVR is easier to apply if excellent observation data is available with good features selection. The maximum discharge of the validation typhoon events was far more significant than the events used for model calibration. Nevertheless, the SVR model could still give an excellent rainfall-runoff estimation.
- In the study, the HEC-HMS model parameters estimation by the SVR model was applied, more data-driven based inference of HEC-HMS model parameters is worth studying.
- This study does not include kernel function selection and its related parameter determination of the SVR model; it is worthwhile to identify a suitable kernel function and other proper parameters in future studies.
- Selection criteria of the feature dataset for SVR model training could be discussed more.
- Observation typhoon data was applied in the study; different rainfall patterns and more training and testing events are necessary to validate the comparison results in future studies.
- The study area is a small rural basin in Northern Taiwan; a more complex and meteorologically different basin could be the future topic of study.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|01A210||Da Bao||600 m||292,574.12||2,753,355.88|
|1140H049||Hen Chi||21.740 m||289,877.43||2,758,720.00|
|Land-Use Category||Percentage %|
|Land for transportation||0.81|
|Water Conservancy-Use Land||0.64|
|Public facilities usage land||0.11|
|Recreational usage land||0.07|
|Mineral usage land||1.54|
|Soil Type||Percentage %|
|Clay Loam, Silty Clay Loam||9.66|
|Typhoon Number||Typhoon Name||Max Rainfall|
|Typhoon Number||Typhoon Name||Max Rainfall|
|Element||Parameter for Optimization|
|sub_1||SCS Curve Number—Initial Abstraction|
|sub_2||SCS Curve Number—Initial Abstraction|
|sub_3||SCS Curve Number—Initial Abstraction|
|sub_1||Clark Unit Hydrograph—Storage Coefficient|
|sub_2||Clark Unit Hydrograph—Storage Coefficient|
|sub_3||Clark Unit Hydrograph—Storage Coefficient|
|sub_1||Clark Unit Hydrograph—Time of Concentration|
|sub_2||Clark Unit Hydrograph—Time of Concentration|
|sub_3||Clark Unit Hydrograph—Time of Concentration|
|sub_1||Recession—Ratio to Peak|
|sub_2||Recession—Ratio to Peak|
|sub_3||Recession—Ratio to Peak|
|Typhoon Number||Typhoon Name||Nash–Sutcliffe Coefficient after Optimization|
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Chiang, S.; Chang, C.-H.; Chen, W.-B. Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan. Water 2022, 14, 191. https://doi.org/10.3390/w14020191
Chiang S, Chang C-H, Chen W-B. Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan. Water. 2022; 14(2):191. https://doi.org/10.3390/w14020191Chicago/Turabian Style
Chiang, Shen, Chih-Hsin Chang, and Wei-Bo Chen. 2022. "Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan" Water 14, no. 2: 191. https://doi.org/10.3390/w14020191