首页|Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm
Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm
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NSTL
Elsevier
Urban flooding is a global metropolitan problem; therefore, establishing reliable streamflow forecasting models is critical for flood control and planning in urban areas. Furthermore, assessing the importance and uncertainty of model predictors is useful for managers; however, these predictors are still underevaluated. To address these concerns, we developed a novel hybrid model, GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting. A case study was conducted in the Jungrang urban basin, which is located on the Han River in South Korea. The model was built and evaluated based on data collected during 39 heavy rain events from 2003 to 2020. To compare the model's forecast capability, a support vector regression model hybridized with a genetic algorithm (GA-SVR) and a multiple linear regression (MLR) model was constructed. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. The results illustrated that the GA-BART model outperformed the GA-SVR and MLR models in multistep-ahead streamflow forecasts, with improved measures of the root mean square error (RMSE), mean absolute error (MAE), relative error, Nash-Sutcliffe efficiency (NSE), time lag and correlation coefficient (CC). In addition, the GA-BART model could reasonably determine the relative importance of the input variables. This study demonstrated that, despite some disadvantages in the five- and six-hour step-ahead forecasts, the hybrid GA-BART model can be a good option among the available models for hourly streamflow forecasting.
NSTL
Elsevier
