Pub Date : 2023-08-28DOI: 10.1080/21664250.2023.2246282
Md. Nur Hossain, S. Araki
{"title":"Estimation of air-bubble-induced wave height and set-up using representative wave approach","authors":"Md. Nur Hossain, S. Araki","doi":"10.1080/21664250.2023.2246282","DOIUrl":"https://doi.org/10.1080/21664250.2023.2246282","url":null,"abstract":"","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47196682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-22DOI: 10.1080/21664250.2023.2236345
H. Karjoun, A. Beljadid
{"title":"A numerical model for predicting waves run-up on coastal areas","authors":"H. Karjoun, A. Beljadid","doi":"10.1080/21664250.2023.2236345","DOIUrl":"https://doi.org/10.1080/21664250.2023.2236345","url":null,"abstract":"","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46096952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-21DOI: 10.1080/21664250.2023.2246286
Y. Androulidakis, C. Makris, Z. Mallios, Y. Krestenitis
{"title":"Sea level variability and coastal inundation over the northeastern Mediterranean Sea","authors":"Y. Androulidakis, C. Makris, Z. Mallios, Y. Krestenitis","doi":"10.1080/21664250.2023.2246286","DOIUrl":"https://doi.org/10.1080/21664250.2023.2246286","url":null,"abstract":"","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43877943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.1080/21664250.2023.2244751
N. Takagaki, N. Suzuki, K. Iwano, Kazuki Nishiumi, Ryota Hayashi, N. Kurihara, Kosuke Nishitani, Takumi Hamaguchi
{"title":"Fetch effects on air-sea momentum transfer at very high wind speeds","authors":"N. Takagaki, N. Suzuki, K. Iwano, Kazuki Nishiumi, Ryota Hayashi, N. Kurihara, Kosuke Nishitani, Takumi Hamaguchi","doi":"10.1080/21664250.2023.2244751","DOIUrl":"https://doi.org/10.1080/21664250.2023.2244751","url":null,"abstract":"","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42177177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-26DOI: 10.1080/21664250.2023.2238949
F. Feddersen, Andreia Amador, Kanoa Pick, A. Vizuet, Kaden Quinn, Eric Wolfinger, J. MacMahan, A. Fincham
{"title":"The wavedrifter: a low-cost IMU-based Lagrangian drifter to observe steepening and overturning of surface gravity waves and the transition to turbulence","authors":"F. Feddersen, Andreia Amador, Kanoa Pick, A. Vizuet, Kaden Quinn, Eric Wolfinger, J. MacMahan, A. Fincham","doi":"10.1080/21664250.2023.2238949","DOIUrl":"https://doi.org/10.1080/21664250.2023.2238949","url":null,"abstract":"","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45587483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1080/21664250.2023.2233312
Tae-Yoo Kim, Woo-Dong Lee
ABSTRACT Appropriate estimation and prediction of wave overtopping discharges are very important in terms of economics, port structure stability, and port operation. In recent years, machine learning (ML) techniques, which predict by finding statistical structures from input/output data using computers, have generated interest. However, as the complexity of ML models increases, interpreting their results becomes increasingly difficult. Interpretation of ML results is an important part in developing an efficient structure design strategy for improved wave overtopping discharge estimation. Therefore, in this study, eight linear/nonlinear ML models were applied to the same data, and a pipeline model for selecting an ML model suitable for data characteristics was developed. In addition, the importance of variables related to the prediction of wave overtopping discharges and their correlations were analyzed by interpretable ML. The research results showed that the extreme gradient boosting model had the highest prediction accuracy and significantly reduced the error. Accordingly, a data-based model can be a new alternative for analyzing the complex physical relationships in the field of coastal engineering and used as a starting point toward structure design and development for coastal disaster prevention.
{"title":"Prediction of wave overtopping discharges at coastal structures using interpretable machine learning","authors":"Tae-Yoo Kim, Woo-Dong Lee","doi":"10.1080/21664250.2023.2233312","DOIUrl":"https://doi.org/10.1080/21664250.2023.2233312","url":null,"abstract":"ABSTRACT Appropriate estimation and prediction of wave overtopping discharges are very important in terms of economics, port structure stability, and port operation. In recent years, machine learning (ML) techniques, which predict by finding statistical structures from input/output data using computers, have generated interest. However, as the complexity of ML models increases, interpreting their results becomes increasingly difficult. Interpretation of ML results is an important part in developing an efficient structure design strategy for improved wave overtopping discharge estimation. Therefore, in this study, eight linear/nonlinear ML models were applied to the same data, and a pipeline model for selecting an ML model suitable for data characteristics was developed. In addition, the importance of variables related to the prediction of wave overtopping discharges and their correlations were analyzed by interpretable ML. The research results showed that the extreme gradient boosting model had the highest prediction accuracy and significantly reduced the error. Accordingly, a data-based model can be a new alternative for analyzing the complex physical relationships in the field of coastal engineering and used as a starting point toward structure design and development for coastal disaster prevention.","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47290403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1080/21664250.2023.2233724
Pavitra Kumar, N. Leonardi
ABSTRACT As climate-driven risks for the world’s coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change. Artificial Intelligence is a powerful technology that has been rapidly evolving recently and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of Artificial Intelligence models were tested, one set relying on localized modeling outputs or localized data sources and another set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing, respectively. The second set of reduced dependency models provides regression values greater than 0.84 and 0.76 for training and testing, respectively. Our results highlight the potential of AI and statistical models for coastal applications.
{"title":"Coastal forecast through coupling of Artificial Intelligence and hydro-morphodynamical modelling","authors":"Pavitra Kumar, N. Leonardi","doi":"10.1080/21664250.2023.2233724","DOIUrl":"https://doi.org/10.1080/21664250.2023.2233724","url":null,"abstract":"ABSTRACT As climate-driven risks for the world’s coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change. Artificial Intelligence is a powerful technology that has been rapidly evolving recently and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of Artificial Intelligence models were tested, one set relying on localized modeling outputs or localized data sources and another set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing, respectively. The second set of reduced dependency models provides regression values greater than 0.84 and 0.76 for training and testing, respectively. Our results highlight the potential of AI and statistical models for coastal applications.","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49064342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1080/21664250.2023.2228005
T. Iwamoto, T. Takagawa, T. Shibayama, M. Esteban, Martin Mäll
ABSTRACT The actions of wind and atmospheric pressure associated with tropical cyclones (e.g. typhoons) are considered the primary factors behind the generation of storm surges, though the fields used in meteorological models can sometimes deviate from observations. To improve these, the direct modification method (DMM) has been previously proposed, though this only modifies the wind field of a typhoon, and further development is necessary for applying it to storm surge hindcasts. The present work describes the development of a semi-empirical gradient wind balance-based method (GWB-M) for modifying both the wind and pressure fields in meteorological models, based on the dynamic relationship between the wind and pressure in typhoons (i.e. gradient wind balance). The applicability of GWB-M was assessed through a storm surge hindcast based on Typhoon Faxai in 2019, which generated powerful waves and a storm surge at Tokyo Bay. GWB-M improved the time series of 10 m wind speed and sea level pressure, with their spatial distributions being more realistic than those in DMM and blending parametric typhoon models (BM), which cannot take into account the influence of the complex topography around Tokyo Bay. Further, the maximum sea level anomalies after the typhoon made landfall were also captured by GWB-M with a higher accuracy than DMM.
{"title":"A proposal of a semi-empirical method for modifying the atmospheric pressure and wind fields of tropical cyclones","authors":"T. Iwamoto, T. Takagawa, T. Shibayama, M. Esteban, Martin Mäll","doi":"10.1080/21664250.2023.2228005","DOIUrl":"https://doi.org/10.1080/21664250.2023.2228005","url":null,"abstract":"ABSTRACT The actions of wind and atmospheric pressure associated with tropical cyclones (e.g. typhoons) are considered the primary factors behind the generation of storm surges, though the fields used in meteorological models can sometimes deviate from observations. To improve these, the direct modification method (DMM) has been previously proposed, though this only modifies the wind field of a typhoon, and further development is necessary for applying it to storm surge hindcasts. The present work describes the development of a semi-empirical gradient wind balance-based method (GWB-M) for modifying both the wind and pressure fields in meteorological models, based on the dynamic relationship between the wind and pressure in typhoons (i.e. gradient wind balance). The applicability of GWB-M was assessed through a storm surge hindcast based on Typhoon Faxai in 2019, which generated powerful waves and a storm surge at Tokyo Bay. GWB-M improved the time series of 10 m wind speed and sea level pressure, with their spatial distributions being more realistic than those in DMM and blending parametric typhoon models (BM), which cannot take into account the influence of the complex topography around Tokyo Bay. Further, the maximum sea level anomalies after the typhoon made landfall were also captured by GWB-M with a higher accuracy than DMM.","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45503082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-27DOI: 10.1080/21664250.2023.2217992
S. Pascolo, M. Petti, S. Bosa
ABSTRACT Predicting wind waves within confined and shallow basins is very important, given the decisive role they play in the resuspension mechanisms of sediments and nutrients from the bottom, on which the main morphological and environmental changes depend. Pascolo, Petti, and Bosa (2019) proposed a set of wave forecasting curves for fully developed conditions in finite depth, which consider the bottom roughness as an additional variable, since it plays a fundamental role in the wave energy dissipation during the generation process. The present study incorporates and integrates the results previously obtained by Pascolo, Petti, and Bosa (2019) and provides the growth curves in the complete form, taking into account also the limitation on fetch. A numerical approach on a simplified domain has been adopted and statistical analyses on the fit of the curves to numerical results have been performed. The new set of equations confirms the variability of the wave heights and periods as a function of the bottom conditions, which can change due to the presence of bedforms, vegetation, or particle size differences. Applications at different conditions of depth, fetch, and roughness have been analyzed, in order to confirm the validity of the new growth curves.
{"title":"New Fetch- and Depth-Limited Forecasting Curves Depending on Bed Roughness","authors":"S. Pascolo, M. Petti, S. Bosa","doi":"10.1080/21664250.2023.2217992","DOIUrl":"https://doi.org/10.1080/21664250.2023.2217992","url":null,"abstract":"ABSTRACT Predicting wind waves within confined and shallow basins is very important, given the decisive role they play in the resuspension mechanisms of sediments and nutrients from the bottom, on which the main morphological and environmental changes depend. Pascolo, Petti, and Bosa (2019) proposed a set of wave forecasting curves for fully developed conditions in finite depth, which consider the bottom roughness as an additional variable, since it plays a fundamental role in the wave energy dissipation during the generation process. The present study incorporates and integrates the results previously obtained by Pascolo, Petti, and Bosa (2019) and provides the growth curves in the complete form, taking into account also the limitation on fetch. A numerical approach on a simplified domain has been adopted and statistical analyses on the fit of the curves to numerical results have been performed. The new set of equations confirms the variability of the wave heights and periods as a function of the bottom conditions, which can change due to the presence of bedforms, vegetation, or particle size differences. Applications at different conditions of depth, fetch, and roughness have been analyzed, in order to confirm the validity of the new growth curves.","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49089437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-22DOI: 10.1080/21664250.2023.2212860
T. Tsubono, Teruhisa Okada, Yasuo Niida, Yuya Kino, N. Nakashiki
ABSTRACT This paper proposes a generalized Green’s Function Approach (GFA) to calibrate the boundary conditions and parameters of a coastal current model. The GFA uses a pseudoinverse for the calculation of control variables, including the boundary conditions and parameters, and a Green’s function matrix, which is the response matrix of sensitivity experiments to the control variables. The GFA was applied to optimize tidal and tidal residual currents in a coastal region with a model simulating the thermal effluent discharged from a power plant. The GFA could be used robustly, regardless of the number of sensitivity analyses, and provided optimal increments for the control variables using a given threshold for the pseudoinverse. The optimization provided the appropriate sea surface conditions to reproduce tidal and tidal residual currents that were consistent with observations. The optimized model allowed an effective and accurate assessment of the environmental impact of the thermal effluent because tidal and tidal residual currents play an important role in the advection and diffusion of thermal effluent.
{"title":"Application of a generalized Green’s function approach to optimize modeled tidal and tidal residual currents for assessment of the dispersion area of thermal effluent discharges","authors":"T. Tsubono, Teruhisa Okada, Yasuo Niida, Yuya Kino, N. Nakashiki","doi":"10.1080/21664250.2023.2212860","DOIUrl":"https://doi.org/10.1080/21664250.2023.2212860","url":null,"abstract":"ABSTRACT This paper proposes a generalized Green’s Function Approach (GFA) to calibrate the boundary conditions and parameters of a coastal current model. The GFA uses a pseudoinverse for the calculation of control variables, including the boundary conditions and parameters, and a Green’s function matrix, which is the response matrix of sensitivity experiments to the control variables. The GFA was applied to optimize tidal and tidal residual currents in a coastal region with a model simulating the thermal effluent discharged from a power plant. The GFA could be used robustly, regardless of the number of sensitivity analyses, and provided optimal increments for the control variables using a given threshold for the pseudoinverse. The optimization provided the appropriate sea surface conditions to reproduce tidal and tidal residual currents that were consistent with observations. The optimized model allowed an effective and accurate assessment of the environmental impact of the thermal effluent because tidal and tidal residual currents play an important role in the advection and diffusion of thermal effluent.","PeriodicalId":50673,"journal":{"name":"Coastal Engineering Journal","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49206942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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