{"refrec":{"BRefID":345097,"RR":"<b>Tiggeloven, T.; Couasnon, A.; van Straaten, C.; Muis, S.; Ward, P.J.</b> (2021). Exploring deep learning capabilities for surge predictions in coastal areas. <i>NPG Scientific Reports 11(1)</i>: 17224. <a href=\"https://dx.doi.org/10.1038/s41598-021-96674-0\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-021-96674-0</a>","BEntID":341732,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>NPG Scientific Reports 11(1)</i>: 17224. <a href=\"https://dx.doi.org/10.1038/s41598-021-96674-0\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-021-96674-0</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Tiggeloven, T.; Couasnon, A.; van Straaten, C.; Muis, S.; Ward, P.J.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Tiggeloven, T. <i>et al.</i>","Englishabstract":"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.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Exploring deep learning capabilities for surge predictions in coastal areas","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-16 01:33:27.673048","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2021,"MonPub":null,"DateUpdate":"2021-09-20","DateCreate":"2021-09-20","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000691021200030","VABBcode":null,"OpenAcc":1,"DOI":"10.1038/s41598-021-96674-0"},"refs":null,"anarec":{"AnaID":345097,"PubliDate":2021,"Pagination":"17224","XtraPublOfAnaID":null,"ISBN":null,"Volume":"11","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":208093,"SerRR":"Scientific Reports (Nature Publishing Group). Nature Publishing Group: London.  ISSN 2045-2322; e-ISSN 2045-2322","StandardTitleSer":"Scientific Reports (Nature Publishing Group)","ISSN":"2045-2322","AbbrevSer":"NPG Scientific Reports","StandardTitleMon":null,"StartPage":17224,"Pages":null,"ToPubliDate":null,"BRefBibLvlCode":"S","SerNotes":null},"monrec":null,"serrec":null,"relations":null,"relationsRev":null,"addrec":null,"othpubs":null,"ownerships":null,"authors":[{"AutName":"Tiggeloven","Firstname":"Timothy","Initials":"T.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":341732,"AutID":465893,"OrderNr":1,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"Couasnon","Firstname":"Anais","Initials":"A.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":341732,"AutID":465894,"OrderNr":2,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"van Straaten","Firstname":"Chiem","Initials":"C.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":341732,"AutID":465895,"OrderNr":3,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"Muis","Firstname":"Sanne","Initials":"S.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":341732,"AutID":233224,"OrderNr":4,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"Ward","Firstname":null,"Initials":"P.J.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":341732,"AutID":83207,"OrderNr":5,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null}],"mapdetails":null,"datasets":null,"monographs":null,"monparts":null,"serparts":null,"BEntOpen":null,"BEntPrivate":null,"availability":[{"BInstID":368198,"LibID":36,"BRefID":345097,"EmbargoDate":null,"FullEmbargoDate":null,"PhysMedID":16,"hasOCRd":1,"ShelfLocCode":"368198","RFID":null,"PaidValue":null,"Medium":"Server","Description":"VLIZ Open Access","Acronym":"VLIZ","Library":"Vlaams Instituut voor de Zee","DutchTerm":"Open access","URL":null,"ClassifID":53,"Classification":"Open access","ReqLink":null,"ClassifTypID":1,"URLLocation":"https://www.vliz.be/imisdocs/publications/","SubDir":null,"InternalReq":0,"LoggedInReq":0,"Disclaimer":null,"DutchDisclaimer":null,"FileFormat":".pdf","FileDescr":"pdf","InsPub":1,"InsID":36,"FileFormID":6,"LendableFlag":1,"PublicFlag":1,"orderLib":"A","Notes":null,"AccConID":null,"AccessConstraint":null,"LicURL":null}],"litstyles":null,"thespers":null,"arch2discl":null,"SERpubls":[{"PublName":"Nature Publishing Group","City":"London"}],"MONpubls":null,"pictures":[],"thestermsPath":null,"thestermsASFA":null,"taxtermsASFA":null,"geotermsASFA":null,"collections":null,"conf":null,"proj":null,"Physdatasets":null,"spcols":null,"doi":null,"publs":null,"serparttypes":null,"monauthors":null,"MParts":null,"SParts":null,"hLibs":null,"langs":[{"BEntID":341732,"AbstractFlag":0,"LangID":15,"LangCode":"en","Lang":"English","DutchTerm":"Engels","LangCodeExtended":"eng"},{"BEntID":341732,"AbstractFlag":1,"LangID":15,"LangCode":"en","Lang":"English","DutchTerm":"Engels","LangCodeExtended":"eng"}],"urls":[{"URL":"https://dx.doi.org/10.1038/s41598-021-96674-0","externalID":"10.1038/s41598-021-96674-0","URLTypeCode":"DOI","URLID":96819,"URLTypID":13,"URLType":"DOI","URLPrefix":"http://dx.doi.org/"}],"thesterms":null,"taxterms":null,"geoterms":null,"othterms":null,"asfacodes":null,"asfa2codes":null,"thestermsFRIS":null,"taxtermsFRIS":null,"geotermsFRIS":null,"othtermsFRIS":null,"resmessage":"","complete":1,"sessions":{"newSesName":"Chisala, Chilekwa, C.","newSesDate":{"date":"2021-09-20 09:31:24.313000","timezone_type":3,"timezone":"Europe/Brussels"},"updSesName":"Chisala, Chilekwa, C.","updSesDate":{"date":"2021-09-20 09:31:24.313000","timezone_type":3,"timezone":"Europe/Brussels"}}}