Abstract
Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.
Highlights
Implementing a stable model to forecast streamflow can be influential for the fields of hydrology and water resources researches [1,2,3,4]
This study revealed that the evolutionary polynomial regression (EPR) model outperformed the multilayer perceptron neural network (MLPNN) and optimally pruned extreme learning machine (OPELM) models to forecast daily streamflow
The BMA2 model supplied the best forecasting accuracy compared to the other models, whereas the M5-based models (i.e., MARS5, M5Tree5, KELM5, and BMA5) showed the worst performance considering all models and categories
Summary
Implementing a stable model to forecast streamflow can be influential for the fields of hydrology and water resources researches [1,2,3,4]. Streamflow forecasting using different antecedent times can be categorized as universal assignment for hydrology and water resources researches [12,13,14,15,16]. Al-Sudani et al [17] surveyed the ability of MARS incorporated with differential evolution (MARS-DE) model to forecast streamflow in Tigris River, Iraq. Adamowski et al [18] managed the MARS model to forecast streamflow in Himalayan watershed, Uttaranchal State, India. They found that the MARS model preformed a superior forecasted accuracy compared to the artificial neural network (ANN) model. The MARS model, did not improve the performance of linear regression model obviously compared to the other models (e.g., extremely randomized trees, XGBoost, and polyMARS)
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