These results provided valuable information on the relative importance of different input variables in the life-table model and make it possible to focus on those variables and uncertainties that have the highest impact on to life-table model results and, thus, on decision making.
To achieve these goals, shaking table model tests and laboratory shear tests were conducted by taking fill dams as an example target structure.
It is now possible to factor the effects observed during in vitro fertilization experiments into the available fish life-table models, and this development has been identified as a key science need.
However, taken together, it could be concluded that lag is not an important input variable for the fine particle life-table models and therefore it could be ignored in future analyses.
In the table, model prediction precision and accuracy are evaluated by relative root mean square error (RMSE %) and relative mean difference (D %).
In this paper, in order to investigate the law of seismic response of underground structure-soil-surface structure interaction system, a shaking table model test was designed and implemented.
From the Table, model F value of 67.97 and value "Prob > F" of 0.0001, implies the model was significant.
The conditional probability tables, model predictions, and management recommendations were compared and shown to be different among models.
The outputs of the test and control models are obtained based on the parameter sets listed in Table 1 (ODE model) and Table 3 (GLM).
These are Linear Regression (LR), Multi Layer Perceptron (MLP), Pace Regression (PR), KStar, Regression by discretization (RD), Random SubSpace (RS), M5P, RepTree and Decision Table models.
