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• W2178734013 abstract "A novel implementation of Dynamic Linear Bayesian Models (DLBM), using either a Varying Coefficient Regression (VCR) or a Discount Weighted Regression (DWR) algorithm was used in the hydrological modeling of annual hydrographs as well as 1-, 2-, and 3-day lead time stream flow forecasting. Using hydrological data (daily discharge, rainfall, and mean, maximum and minimum air temperatures) from the Upper Narew River watershed in Poland, the forecasting performance of DLBM was compared to that of traditional multiple linear regression (MLR) and more recent artificial neural network (ANN) based models. Model performance was ranked DLBM-DWR > DLBM-VCR > MLR > ANN for both annual hydrograph modeling and 1-, 2-, and 3-day lead forecasting, indicating that the DWR and VCR algorithms, operating in a DLBM framework, represent promising new methods for both annual hydrograph modeling and short-term stream flow forecasting." @default.
• W2178734013 created "2016-06-24" @default.
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• W2178734013 date "2015-11-01" @default.
• W2178734013 modified "2023-10-12" @default.
• W2178734013 title "The application of Dynamic Linear Bayesian Models in hydrological forecasting: Varying Coefficient Regression and Discount Weighted Regression" @default.
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• W2178734013 doi "https://doi.org/10.1016/j.jhydrol.2015.10.023" @default.
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