Research Article
Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time
Table 6
Model accuracy results for (a) training and (b) validating data by using only data-driven GRNN model (using the 10-fold cross-validation method) [
63].
| | (a) Training data | | Mean target value for input data | 286.90647 | | Mean target value for predicted values | 286.19978 | | Variance in input data | 99430.392 | | Residual (unexplained) variance after model fit | 2319.0624 | | Proportion of variance explained by model (R2) | 0.97668 (97.668%) | | Coefficient of variation (CV) | 0.167848 | | Normalized mean square error (NMSE) | 0.023323 | | Correlation between actual and predicted target values | 0.988279 | | Maximum error | 300.05113 | | RMSE (root mean squared error) | 48.156644 | | MSE (mean squared error) | 2319.0624 | | MAE (mean absolute error) | 21.681144 | | MAPE (mean absolute percentage error) | 19.471364 |
| | (b) Validation data | | Mean target value for input data | 286.90647 | | Mean target value for predicted values | 285.08662 | | Variance in input data | 99430.392 | | Residual (unexplained) variance after model fit | 24222.689 | | Proportion of variance explained by model (R2) | 0.75639 (75.639%) | | Coefficient of variation (CV) | 0.542464 | | Normalized mean square error (NMSE) | 0.243615 | | Correlation between actual and predicted target values | 0.879069 | | Maximum error | 1002 | | RMSE (root mean squared error) | 155.6364 | | MSE (mean squared error) | 24222.689 | | MAE (mean absolute error) | 74.291454 | | MAPE (mean absolute percentage error) | 31.79981 |
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