The interactions between water, sediment and biology in fluvial systems are complex and driven by multiple forcing mechanisms across a range of spatial and temporal scales.
We discuss suitable comparisons to paleo-data and the appropriateness of model experimental design for single and multiple forcing runs using time-slices and transient simulations.
This study presents an application of the artificial neural network (ANN) to assess salinity variation responding to the multiple forcing functions of freshwater input, tide, and wind in Apalachicola River, Florida.
The results indicate that the ANN model is capable of correlating the non-linear time series of salinity to the multiple forcing signals of wind, tides, and freshwater input in the Apalachicola River.
The general case involving almost periodic forcing is much more complicated due to the presence of multiple forcing frequencies, the topological complexity of the forcing space, and the possible loss of mean motion property.
The resulting HSFEM involves two stages and suits the multi-query setting: in the offline stage, the local stochastic structure of the solution space is identified; in the online stage, the equation can be efficiently solved for multiple forcing functions.
