Willian Weber de Melo , José Pinho , Isabel Iglesias
{"title":"A data model to forecast the morphological evolution of multiple beach profiles","authors":"Willian Weber de Melo , José Pinho , Isabel Iglesias","doi":"10.1016/j.coastaleng.2024.104574","DOIUrl":null,"url":null,"abstract":"<div><p>Beaches are a natural defense against extreme events, such as storms and hurricanes, whose intensity and frequency are expected to increase in the future due to climate change. In this context, models that forecast the morphological evolution of coastal areas can be used to anticipate the effects of future scenarios, allowing early action to mitigate the damage caused by extreme events. Hence, this study included data from three different monitoring programs in data models to simulate the seasonal morphological evolution of several Portuguese beaches. Two different data models were implemented using the Random Forest algorithm. One was fed with profile data and wave conditions while the other considered also sediment size data. Both models achieved suitable performances, but the inclusion of sediment data reduced the model errors and variance, and thus improved model performance. It was demonstrated that combining data from multidisciplinary campaigns can be a solution to generate reliable and robust morphological forecasting models.</p></div>","PeriodicalId":50996,"journal":{"name":"Coastal Engineering","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378383924001224/pdfft?md5=7719bc3324d28e370bef02f89442e2da&pid=1-s2.0-S0378383924001224-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Coastal Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378383924001224","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Beaches are a natural defense against extreme events, such as storms and hurricanes, whose intensity and frequency are expected to increase in the future due to climate change. In this context, models that forecast the morphological evolution of coastal areas can be used to anticipate the effects of future scenarios, allowing early action to mitigate the damage caused by extreme events. Hence, this study included data from three different monitoring programs in data models to simulate the seasonal morphological evolution of several Portuguese beaches. Two different data models were implemented using the Random Forest algorithm. One was fed with profile data and wave conditions while the other considered also sediment size data. Both models achieved suitable performances, but the inclusion of sediment data reduced the model errors and variance, and thus improved model performance. It was demonstrated that combining data from multidisciplinary campaigns can be a solution to generate reliable and robust morphological forecasting models.
期刊介绍:
Coastal Engineering is an international medium for coastal engineers and scientists. Combining practical applications with modern technological and scientific approaches, such as mathematical and numerical modelling, laboratory and field observations and experiments, it publishes fundamental studies as well as case studies on the following aspects of coastal, harbour and offshore engineering: waves, currents and sediment transport; coastal, estuarine and offshore morphology; technical and functional design of coastal and harbour structures; morphological and environmental impact of coastal, harbour and offshore structures.