We show that by introducing the damage variable into the models, prediction error can be reduced, and that accuracy of loss model estimation is site dependent and requires regional data on earthquake losses to improve.
Fig. 5 Taper model prediction errors (predicted minus observed) for total aboveground volume calculated from diameters predicted by the BASE (a), GB99 (b) and M3_HABH (c) stem taper models for trees in the split-validation data set.
This process was repeated until the parameters could be estimated with sufficient accuracy, apparent by the model prediction errors.
The basic principle of MDL estimates both the cost of specifying the model parameters and the associated model prediction errors [27].
Weighting areal contributions by annual rainfall and NPP captured field observations within model prediction errors suggesting that plant productivity may influence source strength.
Through a retrospective model-data comparison, results show that the inclusion of the new monthly IGNS index in place of the current 12-month smoothed IG index reduce the foF2 model prediction errors by nearly a factor of two.
The resulting regional regression models summarized for major US water resources regions 1 8, exhibited prediction R2 values ranging from 76.9% to 92.7% and corresponding average model prediction errors ranging from 56.5% to 124.3%.
At plot-level, error in biomass estimates can be divided into: (1) model error, such as the choice of a particular allometric model, prediction errors or error on the model parameters [9, 10], and (2) measurement error on the tree growth variables (e.g. tree diameter or height) or omission of trees.
error in field data, model prediction errors: NFI to LiDAR and LiDAR to satellite imagery) based on all LiDAR-estimated AGB, satellite imagery, Cubist machine learning algorithm, and Monte Carlo analyses Fig. 12 National forest AGB map based on LiDAR-estimated AGB (with uncertainties < 50%), satellite imagery, Cubist machine learning algorithm, and Monte Carlo analyses.
