The use of genetic programming to develop a predictor of swash excursion on sandy beaches

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{nhess-18-599-2018,

• author = "Marinella Passarella and Evan B. Goldstein and Sandro {De Muro} and Giovanni Coco",
• title = "The use of genetic programming to develop a predictor of swash excursion on sandy beaches",
• journal = "Natural Hazards and Earth System Sciences",
• year = "2018",
• volume = "18",
• number = "2",
• pages = "599--611",
• keywords = "genetic algorithms, genetic programming",
• URL = "https://www.nat-hazards-earth-syst-sci.net/18/599/2018/",
• DOI = "doi:10.5194/nhess-18-599-2018",
• size = "13 pages",
• abstract = "We use genetic programming (GP), a type of machine learning (ML) approach, to predict the total and infragravity swash excursion using previously published data sets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this data set to extend the range of measured swash conditions. Using this newly compiled data set we demonstrate that a ML approach can reduce the prediction errors compared to well-established parameterizations and therefore it may improve coastal hazards assessment (e.g. coastal inundation). Predictors obtained using GP can also be physically sound and replicate the functionality and dependencies of previous published formulas. Overall, we show that ML techniques are capable of both improving predictability (compared to classical regression approaches) and providing physical insight into coastal processes.",

}

Genetic Programming entries for Marinella Passarella Evan B Goldstein Sandro De Muro Giovanni Coco

Citations