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Finally, the approaches for ranking of variables (SA based on coefficients of determination) and variance-based methods are carried out based on the surrogate model in order to quantify the global SA.
Selection-based techniques generally select notional conservation areas based on the surrogate, then measure representation, or likelihood of representation, of the test features in those areas [e.g. 24], [25], [28].
The feasible region for the process can then be evaluated based on the surrogate model.
This stability concept, based on the surrogate data strategy from time series analysis, provides learning guarantees for online unsupervised algorithms even in case of time dependency among observations.
This is a multiobjective design optimization problem, and the optimal operational conditions, as well as the optimal geometric parameters, should be obtained by the optimization algorithm based on the surrogate model with high fidelity.
This paper proposes a new optimisation method, the Parallel Asynchronous Surrogate Model (PASM) method, which is based on the surrogate models approximation and takes advantage of the asynchronous, parallel processing threads.
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The approach was based on the surrogate-based method and combines the use of energy balance simulation (ES) and computational fluid dynamics (CFD).
Each optimization cycle consists of analyzing a number of designs, fitting surrogates for the responses, performing optimization based on the surrogates for a candidate optimum, and finally analyzing that candidate.
When selecting reserves based on the surrogates within each study area, we first performed iterative selection of planning units, based on summed irreplaceability [57], until all targets were met for environmental units.
We attempted to overcome this second limitation by using correlations between patterns of summed irreplaceability (method 5) based on the surrogates and test features, effectively considered all possible ways of assembling systems of conservation areas [25], [28], [59].
Our graphic shows that if the potential surrogate endpoint is a perfect correlate for a true endpoint (even if the slopes and intercepts of the lines were unknown) and if the Prentice Criterion holds, one would obtain the correct inference about the true endpoint based on the potential surrogate endpoint.
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