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In recent years, Spatially Distributed Travel Time (SDTT) methods have been developed as an alternative to semi-distributed and distributed models.
Other modellers struggle to apply physically-based, distributed models within complex, three-dimensional heterogeneous landscapes, inducing equifinality and predictive uncertainty problems.
These are models that assume the marker effects to be normally distributed, models that assume a thick-tailed distribution of marker effects, and models that assume a mixture of two distributions.
This next decade belongs to distributed models not centralized ones, to collaboration not control, and to small data not big data.
Furthermore, full distributed models of SPTCs connected with different phase shifters are developed.
After that, two tracking models, centralized and distributed models, are designed for multi-robot tracking.
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Large-scale distributed and semi-distributed models sub-divide the basin at a coarse resolution to improve computational efficiency.
Therefore, modelers often coarsen spatial discretization of large-scale physically-based and spatially-distributed models for reducing the number of unknowns and the execution times.
Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction.
Semi-distributed models are widely used in urban hydrology, supported by the abundance and detail of geographical data.
This study showed that coupling ANN with semi-distributed models can lead to improved daily streamflow predictions in ungauged watersheds.
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