Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Kurniawan:2014:CG,
-
author = "Alamsyah Kurniawan and Seng Keat Ooi and
Vladan Babovic",
-
title = "Improved sea level anomaly prediction through
combination of data relationship analysis and genetic
programming in Singapore Regional Waters",
-
journal = "Computer \& Geosciences",
-
volume = "72",
-
pages = "94--104",
-
year = "2014",
-
ISSN = "0098-3004",
-
DOI = "doi:10.1016/j.cageo.2014.07.007",
-
URL = "http://www.sciencedirect.com/science/article/pii/S0098300414001642",
-
abstract = "With recent advances in measurement and information
technology, there is an abundance of data available for
analysis and modelling of hydrodynamic systems. Spatial
and temporal data coverage, better quality and
reliability of data modelling and data driven
techniques have resulted in more favourable acceptance
by the hydrodynamic community. The data mining tools
and techniques are being applied in variety of
hydro-informatics applications ranging from data mining
for pattern discovery to data driven models and
numerical model error correction. The present study
explores the feasibility of applying mutual information
theory by evaluating the amount of information
contained in observed and prediction errors of
non-tidal barotropic numerical modelling (i.e. assuming
that the hydrodynamic model, available at this point,
is best representation of the physics in the domain of
interest) by relating them to variables that reflect
the state at which the predictions are made such as
input data, state variables and model output. In
addition, the present study explores the possibility of
employing `genetic programming' (GP) as an offline data
driven modelling tool to capture the sea level anomaly
(SLA) dynamics and then using them for updating the
numerical model prediction in real time applications.
These results suggest that combination of data
relationship analysis and GP models helps to improve
the forecasting ability by providing information of
significant predicative parameters. It is found that GP
based SLA prediction error forecast model can provide
significant improvement when applied as data
assimilation schemes for updating the SLA prediction
obtained from primary hydrodynamic models.",
-
keywords = "genetic algorithms, genetic programming, Data model
integration, Average mutual information, Error
forecasting, Tide-surge interaction",
- }
Genetic Programming entries for
Alamsyah Kurniawan
Seng Keat Ooi
Vladan Babovic
Citations