Exploring deep learning capabilities for surge predictions in coastal areas

Abstract To improve coastal adaptation and management, it is critical to better understand and predict the characteristics of sea levels. Here, we explore the capabilities of artificial intelligence, from four deep learning methods to predict the surge component of sea-level variability based on loc...

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Autores principales:	Timothy Tiggeloven, Anaïs Couasnon, Chiem van Straaten, Sanne Muis, Philip J. Ward
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0ed3964985f849d4808e3ae38d7d2888
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spelling	oai:doaj.org-article:0ed3964985f849d4808e3ae38d7d28882021-12-02T18:53:18ZExploring deep learning capabilities for surge predictions in coastal areas10.1038/s41598-021-96674-02045-2322https://doaj.org/article/0ed3964985f849d4808e3ae38d7d28882021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96674-0https://doaj.org/toc/2045-2322Abstract To improve coastal adaptation and management, it is critical to better understand and predict the characteristics of sea levels. Here, we explore the capabilities of artificial intelligence, from four deep learning methods to predict the surge component of sea-level variability based on local atmospheric conditions. We use an Artificial Neural Networks, Convolutional Neural Network, Long Short-Term Memory layer (LSTM) and a combination of the latter two (ConvLSTM), to construct ensembles of Neural Network (NN) models at 736 tide stations globally. The NN models show similar patterns of performance, with much higher skill in the mid-latitudes. Using our global model settings, the LSTM generally outperforms the other NN models. Furthermore, for 15 stations we assess the influence of adding complexity more predictor variables. This generally improves model performance but leads to substantial increases in computation time. The improvement in performance remains insufficient to fully capture observed dynamics in some regions. For example, in the tropics only modelling surges is insufficient to capture intra-annual sea level variability. While we focus on minimising mean absolute error for the full time series, the NN models presented here could be adapted for use in forecasting extreme sea levels or emergency response.Timothy TiggelovenAnaïs CouasnonChiem van StraatenSanne MuisPhilip J. WardNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Timothy Tiggeloven Anaïs Couasnon Chiem van Straaten Sanne Muis Philip J. Ward Exploring deep learning capabilities for surge predictions in coastal areas
description	Abstract To improve coastal adaptation and management, it is critical to better understand and predict the characteristics of sea levels. Here, we explore the capabilities of artificial intelligence, from four deep learning methods to predict the surge component of sea-level variability based on local atmospheric conditions. We use an Artificial Neural Networks, Convolutional Neural Network, Long Short-Term Memory layer (LSTM) and a combination of the latter two (ConvLSTM), to construct ensembles of Neural Network (NN) models at 736 tide stations globally. The NN models show similar patterns of performance, with much higher skill in the mid-latitudes. Using our global model settings, the LSTM generally outperforms the other NN models. Furthermore, for 15 stations we assess the influence of adding complexity more predictor variables. This generally improves model performance but leads to substantial increases in computation time. The improvement in performance remains insufficient to fully capture observed dynamics in some regions. For example, in the tropics only modelling surges is insufficient to capture intra-annual sea level variability. While we focus on minimising mean absolute error for the full time series, the NN models presented here could be adapted for use in forecasting extreme sea levels or emergency response.
format	article
author	Timothy Tiggeloven Anaïs Couasnon Chiem van Straaten Sanne Muis Philip J. Ward
author_facet	Timothy Tiggeloven Anaïs Couasnon Chiem van Straaten Sanne Muis Philip J. Ward
author_sort	Timothy Tiggeloven
title	Exploring deep learning capabilities for surge predictions in coastal areas
title_short	Exploring deep learning capabilities for surge predictions in coastal areas
title_full	Exploring deep learning capabilities for surge predictions in coastal areas
title_fullStr	Exploring deep learning capabilities for surge predictions in coastal areas
title_full_unstemmed	Exploring deep learning capabilities for surge predictions in coastal areas
title_sort	exploring deep learning capabilities for surge predictions in coastal areas
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0ed3964985f849d4808e3ae38d7d2888
work_keys_str_mv	AT timothytiggeloven exploringdeeplearningcapabilitiesforsurgepredictionsincoastalareas AT anaiscouasnon exploringdeeplearningcapabilitiesforsurgepredictionsincoastalareas AT chiemvanstraaten exploringdeeplearningcapabilitiesforsurgepredictionsincoastalareas AT sannemuis exploringdeeplearningcapabilitiesforsurgepredictionsincoastalareas AT philipjward exploringdeeplearningcapabilitiesforsurgepredictionsincoastalareas
_version_	1718377308846292992

Exploring deep learning capabilities for surge predictions in coastal areas

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