Wave Height Quantification Using Land Based Seismic Data with Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Donne:2014:CEC,
-
title = "Wave Height Quantification Using Land Based Seismic
Data with Grammatical Evolution",
-
author = "Sarah Donne and Miguel Nicolau and
Christopher Bean and Michael O'Neill",
-
pages = "2909--2916",
-
booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary
Computation",
-
year = "2014",
-
month = "6-11 " # jul,
-
editor = "Carlos A. {Coello Coello}",
-
address = "Beijing, China",
-
ISBN = "0-7803-8515-2",
-
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Real-world applications",
-
DOI = "doi:10.1109/CEC.2014.6900563",
-
abstract = "Accurate, real time, continuous ocean wave height
measurements are required for the initialisation of
ocean wave forecast models, model hindcasting, and
climate studies. These measurements are usually
obtained using in situ ocean buoys or by satellite
altimetry, but are sometimes incomplete due to
instrument failure or routine network upgrades. In such
situations, a reliable gap filling technique is
desirable to provide a continuous and accurate ocean
wave field record. Recorded on a land based seismic
network are continuous seismic signals known as
microseisms. These microseisms are generated by the
interactions of ocean waves and will be used in the
estimation of ocean wave heights. Grammatical Evolution
is applied in this study to generate symbolic models
that best estimate ocean wave height from terrestrial
seismic data, and the best model is validated against
an Artificial Neural Network. Both models are tested
over a five month period of 2013, and an analysis of
the results obtained indicates that the approach is
robust and that it is possible to estimate ocean wave
heights from land based seismic data.",
-
notes = "WCCI2014",
- }
Genetic Programming entries for
Sarah Donne
Miguel Nicolau
Christopher Bean
Michael O'Neill
Citations