Abstract
To better understand the effect and constraint of different data lengths on the data-driven model training for the rainfall-runoff simulation, the support vector regression (SVR) approach was applied to the data-driven model as the core algorithm in the present study. Various features selection strategies and different data lengths were employed in the training phase of the model. The validated results of the SVR were compared with the rainfall-runoff simulation derived from a physically based hydrologic model, the Hydrologic Modeling System (HEC-HMS). The HEC-HMS was considered a conventional approach and was also calibrated with a dataset period identical to the SVR. Our results showed that the SVR and HEC-HMS models could be adopted for short and long periods of rainfall-runoff simulation. However, the SVR model estimated the rainfall-runoff relationship reasonably well even if the observational data of one year or one typhoon event was used. In contrast, the HEC-HMS model needed more parameter optimization and inference processes to achieve the same performance level as the SVR model. Overall, the SVR model was superior to the HEC-HMS model in the performance of the rainfall-runoff simulation.
Highlights
The HEC-Hydrologic Modeling System (HMS) and support vector regression (SVR), a physically based model and a data-driven hydrologic model were used to identify the adaptability within different lengths of field observation data in the manner of parameters determination and model training
Model validation process: one by using SVR, with optimized parameter values derived from the typhoon events which occurred within a specific time range, i.e., 19, 5, 2, and
1 year(s), with target and maximum rainfall value, rainfall duration, total rainfall depth as feature values, to acquire the model validation parameter values, named Type 1 HECHMS in the study; the other one by manipulating the average optimized parameter values retrieved from the typhoon events which occurred within the same specific time range as
Summary
The precision and robustness of rainfall-runoff simulations are essential to watershed modeling from various perspectives, such as planning and designing soil conservation practices, irrigation water management, wetland restoration, stream restoration, water-table management, and water resources planning, development, and management [1]. There are various styles of rainfall-runoff models worldwide developed to solve these issues from the manners of deterministic, probabilistic, or stochastic approaches [2]. For a well-gauged watershed, a hydrologic model with an appropriate scheme that meets the watershed characteristics can be applied for the rainfall-runoff modeling. For poorly gauged or ungauged watersheds, a physically based hydrologic model is preferred for reasonable parameter estimation processes [3]. For the convenience of applying hydrologic models, a software package with graphic user interface (GUI) makes the rainfall-runoff simulation task easier
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