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Scientists have thus developed spatial models to map cumulative human impacts on marine ecosystems.
Additionally, using hydrodynamic models to map floodplain over large stream network can be computationally challenging.
The present study uses an ensemble of machine learning models to map the extent of artificially drained areas in Denmark.
Precision agriculture managers are calling for advanced satellite models to map field-scale residue mass and quality.
We further developed models to map flooded forest at a riparian site that experienced severe tree dieback.
This study applied four data mining algorithms including AdaBoost, Bagging, generalized additive model (GAM), and Naive Bayes (NB) models to map groundwater potential.
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Data visualization here is broadly defined, encompassing everything from charts and graphs to 3D models to maps to data art.
It gives them a model to map their own mistakes.
The results were used with a variable aperture channel model to map the aperture distribution.
This is notable since it provides a model to map state variables to progress variables that is a closed model.
We propose to use the CPTSM model to map TSM concentrations from satellite data in the ECS.
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