Abstract: All climate models agree that the temperature in
Greece will increase in the range of 1° to 2°C by the year 2030 and
mean sea level in Mediterranean is expected to rise at the rate of 5
cm/decade. The aim of the present paper is the estimation of the
coastline displacement driven by the climate change and sea level
rise. In order to achieve that, all known statistical and non-statistical
computational methods are employed on some Greek coastal areas.
Furthermore, Kalman filtering techniques are for the first time
introduced, formulated and tested. Based on all the above, shoreline
change signals and noises are computed and an inter-comparison
between the different methods can be deduced to help evaluating
which method is most promising as far as the retrieve of shoreline
change rate is concerned.
Abstract: The System Identification problem looks for a
suitably parameterized model, representing a given process. The
parameters of the model are adjusted to optimize a performance
function based on error between the given process output and
identified process output. The linear system identification field is
well established with many classical approaches whereas most of
those methods cannot be applied for nonlinear systems. The problem
becomes tougher if the system is completely unknown with only the
output time series is available. It has been reported that the
capability of Artificial Neural Network to approximate all linear and
nonlinear input-output maps makes it predominantly suitable for the
identification of nonlinear systems, where only the output time series
is available. [1][2][4][5]. The work reported here is an attempt to
implement few of the well known algorithms in the context of
modeling of nonlinear systems, and to make a performance
comparison to establish the relative merits and demerits.