The state and event driven forecasting system implements the input data download, input data provision via SOAP based WEB service and the run of the hydrology model with observed and with predicted NWP meteorology fields.
The control law is derived explicitly offline and simple to be implemented: the input may be computed fast online while the unknown model time constant is estimated at the same time.
The number of parameters scales as M̃×N, where M̃ is the number such that no higher order network with less than M̃ higher order terms can implement the same input data set and N is the dimensionality of the input vectors.
Further improvement of the optimization process can be done in the future study by testing various optimization modules that implement the same input and output interfaces.
In order to be able to implement the proposed optimal input, the adaptive two-stage procedure for generating the input signal is proposed.
For example, to evaluate the practical performance of spatial IA methods, at least six nodes (three transmitters and three receivers) with two antennas each are needed to implement the simplest multiple-input multiple-output (MIMO) interference channel.
In order to have better generalization, the model was designed to realize a supervised learning such that after learning, output for any input vector is the same as the output of a higher order network that implements the same input data set using M̃ number of higher order terms.
When the extracted DPD model is implemented, the actual input signal is the baseband signal.
Based on the four GIS-based 3D slope stability analysis models, a GIS-based program, 3DSlopeGIS, has been developed to implement the algorithm where all the input data are in the same format as the GIS dataset.
To perform such a large number of simulation runs, a computer code was utilised to routinely read input data, implement the simulation runs and produce output data.
