Brief communication  &amp;quot;Evaluating European Coastal Evolution using Bayesian Networks&amp;quot;

Yates, Marissa; Cozannet, Gonéri Le

doi:10.5194/nhess-12-1173-2012

Cited by 35 publications

(45 citation statements)

References 15 publications

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“…decision trees, artificial neural networks, Bayesian networks and evolutionary computation), all of which have shown applicability in coastal settings (e.g. Pape et al, 2007;Knaapen and Hulscher, 2002;Dickson and Perry, 2016;Yates and Le Cozannet, 2012). Previous machine learning work has focused on predicting run-up and swash, but only for engineered structures, impermeable slopes and/or for laboratory experiments (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Passarella

Goldstein

Muro

et al. 2018

Nat. Hazards Earth Syst. Sci.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Passarella

Goldstein

Muro

et al. 2018

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Machine learning, in the context of this paper, defines a suite of algorithms used to develop predictive relationships (correlations) using a set of input data. Examples of commonly used machine learning techniques in the Earth sciences are artificial neural networks (e.g., Maier and Dandy, 2000;Pape et al, 2007;van Maanen et al, 2010), regression trees (e.g., Snelder et al, 2009;Oehler et al, 2012), Bayesian networks (e.g., Aguilera et al, 2011;Yates and Le Cozannet, 2012), and evolutionary algorithms (e.g., Knaapen and Hulscher, 2002;Ruessink, 2005;Goldstein et al, 2013). Machine learning techniques offer insight and are high-performance, reproducible, and scalable.…”

Section: Introductionmentioning

confidence: 99%

Machine learning components in deterministic models: hybrid synergy in the age of data

Goldstein

Coco

2015

Front. Environ. Sci.

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“…Guttiérrez et al 2010, Yates & Le Cozannet 2012, there are 2 main types of vulnerability index: one focussing on the vulnerability of sandy coasts to storms, i.e. a short time scale (e.g.…”

Section: Existing Vulnerability Indexes: Application and Limitsmentioning

confidence: 99%

Vulnerability of sandy coasts to climate variability

Idier¹,

Castelle²,

Poumadère³

et al. 2013

Clim. Res.

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The main objective of the VULSACO (VULnerability of SAndy COasts to climate change and anthropic pressure) project was to investigate present day and potential future vulnerability of sandy coasts at the 2030 horizon, i.e. on a time scale related to climate variability. The method, based on a multidisciplinary approach bringing together geologists, geographers, physicists, social psychologists, engineers and stakeholders, was structured around 4 axes: field data analysis; numerical modelling; analysis of governance and stakeholder perceptions; and development of vulnerability indexes. This approach was designed to investigate vulnerability at a local scale and was applied to 4 contrasting beaches located in France: Sète Lido (Mediterranean Sea), Truc Vert and La Tresson beaches (Atlantic Ocean), and Dewulf (English Channel). The results focus on decadal and multi-annual beach trends at the Truc Vert beach site. There is almost no trend in beach volume at Truc Vert beach, although there is a variation in this parameter on a cycle of 2 to 3 yr, with variations related to wave energy and probably to indexes of climate variability. Numerical modelling identified the sensitivity of beach responses to changes in wave height and direction, especially in terms of subtidal morphology and the potential development of shoreline instability. Together with the observed offshore wave angle at the Biscay Buoy, these model results suggest that a potential change in wave angle due to climate variability could significantly modify the bars' morphology. The combination of data analysis and numerical modelling contributed to the development of vulnerability indexes designed for sandy coasts, which take into account climate-dependant variables such as waves. This allowed the differentiation of the sites in terms of vulnerability to erosion: Sète Lido and Truc Vert beach were the most and least vulnerable sites, respectively. These indexes help in identifying the dominant components of beach vulnerability, and provide potential for the study of how anthropogenic factors affect vulnerability. The study of stakeholder perceptions and decision-making with regard to climate-related risk also highlighted potential anthropogenic effects on beach vulnerability, and identified possible site-specific outcomes.

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Brief communication "Evaluating European Coastal Evolution using Bayesian Networks"

Cited by 35 publications

References 15 publications

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

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

Machine learning components in deterministic models: hybrid synergy in the age of data

Vulnerability of sandy coasts to climate variability

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