Jennifer Montaño, G. Coco, J. Antolínez, Tomas Beuzen, K. Bryan, L. Cagigal, B. Castelle, M. Davidson, E. Goldstein, Rai Ibaceta Vega, D. Idier, B. Ludka, S. Ansari, F. Méndez, B. Murray, N. Plant, A. Robinet, A. Rueda, N. Sénéchal, Joshua A. Simmons, Kristen D. Splinter, S. Stephens, I. Townend, S. Vitousek, Kilian Vos
{"title":"海岸线预测:海岸线模型的盲测","authors":"Jennifer Montaño, G. Coco, J. Antolínez, Tomas Beuzen, K. Bryan, L. Cagigal, B. Castelle, M. Davidson, E. Goldstein, Rai Ibaceta Vega, D. Idier, B. Ludka, S. Ansari, F. Méndez, B. Murray, N. Plant, A. Robinet, A. Rueda, N. Sénéchal, Joshua A. Simmons, Kristen D. Splinter, S. Stephens, I. Townend, S. Vitousek, Kilian Vos","doi":"10.1142/9789811204487_0055","DOIUrl":null,"url":null,"abstract":"Predictions of shoreline change are of great societal importance, but models tend to be tested and tuned for the specific site of interest. To overcome this issue and test the ability of numerical models to simulate shoreline change over the medium scale (order of years) we have organized a non-competitive competition where participants were given data to train their model (1999-2014) and data to predict seasonal to inter-annual future changes (2014-2017). Participants were shown the observed shoreline changes only after submission of their modelling results. Overall, 19 numerical models were tested, the vast majority falling in the broad categories of \"hybrid models\" or \"machine learning\". Models were able to reproduce the mean characteristics of shoreline change but often failed to reproduce the observed rapid changes induced by storms.","PeriodicalId":254775,"journal":{"name":"Coastal Sediments 2019","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"SHORECASTS: A BLIND-TEST OF SHORELINE MODELS\",\"authors\":\"Jennifer Montaño, G. Coco, J. Antolínez, Tomas Beuzen, K. Bryan, L. Cagigal, B. Castelle, M. Davidson, E. Goldstein, Rai Ibaceta Vega, D. Idier, B. Ludka, S. Ansari, F. Méndez, B. Murray, N. Plant, A. Robinet, A. Rueda, N. Sénéchal, Joshua A. Simmons, Kristen D. Splinter, S. Stephens, I. Townend, S. Vitousek, Kilian Vos\",\"doi\":\"10.1142/9789811204487_0055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predictions of shoreline change are of great societal importance, but models tend to be tested and tuned for the specific site of interest. To overcome this issue and test the ability of numerical models to simulate shoreline change over the medium scale (order of years) we have organized a non-competitive competition where participants were given data to train their model (1999-2014) and data to predict seasonal to inter-annual future changes (2014-2017). Participants were shown the observed shoreline changes only after submission of their modelling results. Overall, 19 numerical models were tested, the vast majority falling in the broad categories of \\\"hybrid models\\\" or \\\"machine learning\\\". Models were able to reproduce the mean characteristics of shoreline change but often failed to reproduce the observed rapid changes induced by storms.\",\"PeriodicalId\":254775,\"journal\":{\"name\":\"Coastal Sediments 2019\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Coastal Sediments 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/9789811204487_0055\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Coastal Sediments 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9789811204487_0055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictions of shoreline change are of great societal importance, but models tend to be tested and tuned for the specific site of interest. To overcome this issue and test the ability of numerical models to simulate shoreline change over the medium scale (order of years) we have organized a non-competitive competition where participants were given data to train their model (1999-2014) and data to predict seasonal to inter-annual future changes (2014-2017). Participants were shown the observed shoreline changes only after submission of their modelling results. Overall, 19 numerical models were tested, the vast majority falling in the broad categories of "hybrid models" or "machine learning". Models were able to reproduce the mean characteristics of shoreline change but often failed to reproduce the observed rapid changes induced by storms.
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