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Experimental investigation of flow in a compound channel with symmetric diverging floodplains
Published in Wim Uijttewaal, Mário J. Franca, Daniel Valero, Victor Chavarrias, Clàudia Ylla Arbós, Ralph Schielen, Alessandra Crosato, River Flow 2020, 2020
B.S. Das, K. Devi, J.R. Khuntia, K.K. Khatua
To check the strength of the present model the error analysis is performed in terms of statistical parameters such as mean percentage error (MPE), mean absolute percentage error (MAPE), Root mean square error (RMSE), index of agreement (Id) and Nash-Sutcliffe coefficient (E). The detail definition of different error analysis terms may be found in Das and Khatua (2018a) and Devi et al. (2016). Table 2 shows the error analysis results for the different diverging compound channels. The MPE values lie between -5% to +5% and MAPE values are less than 5% for all diverging compound channels. From Table 2, it also can be seen that the Id and E values are greater than 0.85 for all diverging channel cases which depict the accuracy of the developed model.
Determining the invoicing dates for raw material order and finish product dispatch using neural networks under exchange rate volatility
Published in International Journal of Logistics Research and Applications, 2023
Janith Piyumal Weerasingha, Yapa Mahinda Bandara, Pasan Manuranga Edirisinghe
ADF test is used to test the stationarity of the data, and the second difference was considered to make all series stationary. The stationary dataset was checked for Akaike’s Information Criterion (AIC) test to select the best lag order. AIC test selected lag order as 4. Durban Watson’s Statistic Test is used to check for serial correlation among residuals. Since the dataset is differenced two times to make the time-series stationery, the forecasted data should be transformed back to the original state. Then with those values, the accuracy is tested in the VAR model predictions. Mean Absolute Percentage Error (MAPE), Mean Error (ME), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Root Mean Squared Error (RMSE), and Correlation are used as model evaluation parameters.
Empirical calculation of the optimal tilt angle for solar collectors in northern hemisphere
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Mehmet Ali Kallioğlu, Aydın Durmuş, Hakan Karakaya, Adem Yılmaz
Literature has several different methods to determine statistically accuracy of equations. The most frequently used methods are; mean bias error (MBE), root mean square error (RMSE), t-statistics (t-sat), determination coefficient (R2), mean percentage error (MPE), mean absolute percentage error (MAPE), sum of squares of relative errors (SSRE), and relative squared error (RSE). The chief goal of these methods is to specify usability and accuracy of mathematical correlations developed. (Akinoğlu and Ecevit 1990; Kallioğlu et al. 2017; Liu and Jordan 1961; Ma and Iqbal 1983; Stone 1993).