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This study used CC, RMSE, MAE, RAE, and RRSE to evaluate the forecast mode.
RRSE = [[summation].sup.n.sub.i=1][([x.sub.i] - [y.sub.i]).sup.2]/[[summation].sup.n.sub.i=1][([y.sub.i] - [bar.y]).sup.2], (3)
The two approaches employ Random Forest, RBF Network, Kstar, KNN, and Random Tree to forecast water levels for evaluating the six processed missing values methods under five evaluation indices: CC, RMSE, MAE, RAE, and RRSE. The results are shown in Tables 3 and 4, and we can see that the mean of the nearby points' method wins versus other methods in CC, MAE, RAE, RRSE, and RMSE.
Tables 6 and 7 show that before variable selection (full variables), the Random Forest forecast model has the best forecast performance in CC, RMSE, MAE, RAE, and RRSE indices.
We use RRSE instead of RMSE because the former applies the average value as common reference point, being easy to understand by people unaccustomed to physical crop yield dimensions.
Ties are solved in the following order: RRSE (lower), R (higher), and RMAE (lower).
Table 3 shows every metric obtained per technique (RRSE, R, and RMAE).
Table 6 shows the RRSE, R, and RMAE measures using all the potential attributes as explanatory variables.
RRSE and R measures in Table 8 show the obtained results.
We applied the R measure to the attributes in LAS that intersects the OAS and the RRSE. This allows us to distinguish the error caused by including the relevant attributes in the model and the errors due to the regression technique predictive ability.
ANN and M5' obtained the best prediction, and, between them, the former achieved the lower RRSE, the higher R correlation, and the lower RMAE value.
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- RRSP Contribution
- RRSP Contributions
- RRSP Deduction
- RRSP Deductions