A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks

An integrated methodological approach to the development of a coastal flood early-warning system is presented in this paper to improve societal preparedness for coastal flood events. The approach consists of two frameworks, namely the Hindcast Framework and the Forecast Framework. The aim of the for...

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Autores principales:	Michalis Chondros, Anastasios Metallinos, Andreas Papadimitriou, Constantine Memos, Vasiliki Tsoukala
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	coastal flood inundation risk early-warning system wave overtopping storm surge artificial neural network Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/86f66d3a871d47b49d2e857a93e17c89
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id	oai:doaj.org-article:86f66d3a871d47b49d2e857a93e17c89
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spelling	oai:doaj.org-article:86f66d3a871d47b49d2e857a93e17c892021-11-25T18:04:57ZA Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks10.3390/jmse91112722077-1312https://doaj.org/article/86f66d3a871d47b49d2e857a93e17c892021-11-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1272https://doaj.org/toc/2077-1312An integrated methodological approach to the development of a coastal flood early-warning system is presented in this paper to improve societal preparedness for coastal flood events. The approach consists of two frameworks, namely the Hindcast Framework and the Forecast Framework. The aim of the former is to implement a suite of high-credibility numerical models and validate them according to past flooding events, while the latter takes advantage of these validated models and runs a plethora of scenarios representing distinct sea-state events to train an Artificial Neural Network (ANN) that is capable of predicting the impending coastal flood risks. The proposed approach was applied in the flood-prone coastal area of Rethymno in the Island of Crete in Greece. The performance of the developed ANN is good, given the complexity of the problem, accurately predicting the targeted coastal flood risks. It is capable of predicting such risks without requiring time-consuming numerical simulations; the ANN only requires the offshore wave characteristics (height, period and direction) and sea-water-level elevation, which can be obtained from open databases. The generic nature of the proposed methodological approach allows its application in numerous coastal regions.Michalis ChondrosAnastasios MetallinosAndreas PapadimitriouConstantine MemosVasiliki TsoukalaMDPI AGarticlecoastal floodinundation riskearly-warning systemwave overtoppingstorm surgeartificial neural networkNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1272, p 1272 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	coastal flood inundation risk early-warning system wave overtopping storm surge artificial neural network Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	coastal flood inundation risk early-warning system wave overtopping storm surge artificial neural network Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Michalis Chondros Anastasios Metallinos Andreas Papadimitriou Constantine Memos Vasiliki Tsoukala A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks
description	An integrated methodological approach to the development of a coastal flood early-warning system is presented in this paper to improve societal preparedness for coastal flood events. The approach consists of two frameworks, namely the Hindcast Framework and the Forecast Framework. The aim of the former is to implement a suite of high-credibility numerical models and validate them according to past flooding events, while the latter takes advantage of these validated models and runs a plethora of scenarios representing distinct sea-state events to train an Artificial Neural Network (ANN) that is capable of predicting the impending coastal flood risks. The proposed approach was applied in the flood-prone coastal area of Rethymno in the Island of Crete in Greece. The performance of the developed ANN is good, given the complexity of the problem, accurately predicting the targeted coastal flood risks. It is capable of predicting such risks without requiring time-consuming numerical simulations; the ANN only requires the offshore wave characteristics (height, period and direction) and sea-water-level elevation, which can be obtained from open databases. The generic nature of the proposed methodological approach allows its application in numerous coastal regions.
format	article
author	Michalis Chondros Anastasios Metallinos Andreas Papadimitriou Constantine Memos Vasiliki Tsoukala
author_facet	Michalis Chondros Anastasios Metallinos Andreas Papadimitriou Constantine Memos Vasiliki Tsoukala
author_sort	Michalis Chondros
title	A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks
title_short	A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks
title_full	A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks
title_fullStr	A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks
title_full_unstemmed	A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks
title_sort	coastal flood early-warning system based on offshore sea state forecasts and artificial neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/86f66d3a871d47b49d2e857a93e17c89
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A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks

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