Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/nhess-2017-232
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
04 Oct 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).
The use of genetic programming to develop a predictor of swash excursion on sandy beaches
Marinella Passarella1, Evan B. Goldstein2, Sandro De Muro1, and Giovanni Coco3 1Department of Chemical and Geological Sciences, Coastal and Marine Geomorphology Group (CMGG), Università degli Studi di Cagliari, 09124 Cagliari, Italy
2Department of Geological Sciences, University of North Carolina, 27599, USA
3School of Environment, Faculty of Science, University of Auckland, Auckland, 1142, New Zealand
Abstract. We use Genetic Programming (GP), a type of Machine Learning (ML) approach, to predict the total and infragravity swash excursion using previously published datasets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this dataset to extend the range of measured swash conditions. Using this newly compiled dataset we demonstrate that a ML approach can reduce the prediction errors compared to well-established parameterizations and therefore it contributes to the error in 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.

Citation: Passarella, M., Goldstein, E. B., De Muro, S., and Coco, G.: The use of genetic programming to develop a predictor of swash excursion on sandy beaches, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-232, in review, 2017.
Marinella Passarella et al.
Marinella Passarella et al.
Marinella Passarella et al.

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