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This paper introduces a new approach for constructing TIA by using a dynamic forecasting model based on neural networks.
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Also, the mean pupil diameter was significantly larger when controllers used a static storm forecast tool compared to when controllers used a dynamic forecast tool, indicating a higher workload level during the use of static tools.
The method uses a Dynamic Generalised Linear Model with Bayesian forecasting to account for past survey sample sizes and survey results and forecast future population decay, while quantifying estimation uncertainty.
Here we use a dynamic scaling approach.
A fruitful alternative to using a single forecasting technique is to combine the forecasts from several conceptually different models.
It can also be used a posteriori to guide dose revisions using a Bayesian forecasting method.
Using a consensus of forecast models reduces forecast error.
The goal of the simulation experiment is to determine how well a forecast ensemble tracks the truth when the synthetic observations are assimilated using a forecast model that is identical to the model used for the truth run [ 21].
Using a consensus of forecast models, as well as ensemble members of the various models, can help reduce forecast error.
We constructed a forecasting model using a system dynamics approach.
It would also allow the exploration of gains in forecast accuracy by using a weighted combination of forecasts from several models and/or methods.
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