Wave Height Quantification Using Land Based Seismic Data with Grammatical Evolution

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@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