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We are aware of the following cross-validation pitfalls when selecting and assessing classification and regression models: Selection of variables prior to, and not within, cross-validation.
In the Kennedy and O'Hagan framework for Bayesian calibration of physics models, selection of an appropriate prior form for the model discrepancy function is a challenging issue due to the lack of physics knowledge regarding model inadequacy.
Basic ideas, such as the optimum complexity of models, selection of an effective subset of the measurements, as well as a realistic estimation of the performance of calibration models will be discussed.
Our work also highlights modelling features needed for pedogenesis in general: development of biological and physical models, selection of a comprehensive energy unit, dual-time scale modelling, and multi-scale representation of the soil profile.
The correlation among all factors that influence airfield pavement performance such as load, environment to develop performance models, selection of feasible M&R alternatives, economics, construction and maintenance practices, management systems and their components must also be considered for implementing APMS.
DRN was responsible for overall study design, development of initial simulation models, selection of input parameters, and drafting and editing of manuscript.
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GIS-based layers are imported to the model, selection of 2D modeling tab, generation of new data files, input of meteorological data, creation of drainage infrastructure, and running of simulation.
Moreover, the model selection of the parameters is time-consuming.
Several procedures have been proposed for automatic model selection of tensor decompositions.
The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
The experimental results could provide a reference for the design and model selection of CS-DGS in engineering.
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