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Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting

Dominic Reeve Orcid Logo, Harshinie Karunarathna Orcid Logo, Shunqi Pan, Jose M. Horrillo-Caraballo, Grzegorz Różyński, Roshanka Ranasinghe

Geomorphology, Volume: 256, Pages: 49 - 67

Swansea University Authors: Dominic Reeve Orcid Logo, Harshinie Karunarathna Orcid Logo

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DOI (Published version): 10.1016/j.geomorph.2015.10.016

Abstract

It is now common for coastal planning to anticipate changes anywhere from 70 to 100 years into the future. The process models developed and used for scheme design or for large-scale oceanography are currently inadequate for this task. This has prompted the development of a plethora of alternative me...

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Published in: Geomorphology
Published: 2016
URI: https://cronfa.swan.ac.uk/Record/cronfa24470
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The process models developed and used for scheme design or for large-scale oceanography are currently inadequate for this task. This has prompted the development of a plethora of alternative methods. Some, such as reduced complexity or hybrid models simplify the governing equations retaining processes that are considered to govern observed morphological behaviour. The computational cost of these models is low and they have proven effective in exploring morphodynamic trends and improving our understanding of mesoscale behaviour. One drawback is that there is no generally agreed set of principles on which to make the simplifying assumptions and predictions can vary considerably between models. An alternative approach is data-driven techniques that are based entirely on analysis and extrapolation of observations. Here, we discuss the application of some of the better known and emerging methods in this category to argue that with the increasing availability of observations from coastal monitoring programmes and the development of more sophisticated statistical analysis techniques data-driven models provide a valuable addition to the armoury of methods available for mesoscale prediction. The continuation of established monitoring programmes is paramount, and those that provide contemporaneous records of the driving forces and the shoreline response are the most valuable in this regard. In the second part of the paper we discuss some recent research that combining some of the hybrid techniques with data analysis methods in order to synthesise a more consistent means of predicting mesoscale coastal morphological evolution. While encouraging in certain applications a universally applicable approach has yet to be found. The route to linking different model types is highlighted as a major challenge and requires further research to establish its viability. 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spelling 2020-05-22T15:42:08.1859367 v2 24470 2015-11-17 Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting 3e76fcc2bb3cde4ddee2c8edfd2f0082 0000-0003-1293-4743 Dominic Reeve Dominic Reeve true false 0d3d327a240d49b53c78e02b7c00e625 0000-0002-9087-3811 Harshinie Karunarathna Harshinie Karunarathna true false 2015-11-17 CIVL It is now common for coastal planning to anticipate changes anywhere from 70 to 100 years into the future. The process models developed and used for scheme design or for large-scale oceanography are currently inadequate for this task. This has prompted the development of a plethora of alternative methods. Some, such as reduced complexity or hybrid models simplify the governing equations retaining processes that are considered to govern observed morphological behaviour. The computational cost of these models is low and they have proven effective in exploring morphodynamic trends and improving our understanding of mesoscale behaviour. One drawback is that there is no generally agreed set of principles on which to make the simplifying assumptions and predictions can vary considerably between models. An alternative approach is data-driven techniques that are based entirely on analysis and extrapolation of observations. Here, we discuss the application of some of the better known and emerging methods in this category to argue that with the increasing availability of observations from coastal monitoring programmes and the development of more sophisticated statistical analysis techniques data-driven models provide a valuable addition to the armoury of methods available for mesoscale prediction. The continuation of established monitoring programmes is paramount, and those that provide contemporaneous records of the driving forces and the shoreline response are the most valuable in this regard. In the second part of the paper we discuss some recent research that combining some of the hybrid techniques with data analysis methods in order to synthesise a more consistent means of predicting mesoscale coastal morphological evolution. While encouraging in certain applications a universally applicable approach has yet to be found. The route to linking different model types is highlighted as a major challenge and requires further research to establish its viability. We argue that key elements of a successful solution will need to account for dependencies between driving parameters, (such as wave height and tide level), and be able to predict step changes in the configuration of coastal systems. Journal Article Geomorphology 256 49 67 1 3 2016 2016-03-01 10.1016/j.geomorph.2015.10.016 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University RCUK 2020-05-22T15:42:08.1859367 2015-11-17T11:20:33.1003704 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Dominic Reeve 0000-0003-1293-4743 1 Harshinie Karunarathna 0000-0002-9087-3811 2 Shunqi Pan 3 Jose M. Horrillo-Caraballo 4 Grzegorz Różyński 5 Roshanka Ranasinghe 6 0024470-02022016141015.pdf ReeveDataDrivenAndHybridCoastal2015VOR.pdf 2016-02-02T14:10:15.1670000 Output 2964927 application/pdf Version of Record true 2016-02-02T00:00:00.0000000 This is an open access article under the CC-BY license. true http://creativecommons.org/licenses/by/4.0
title Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
spellingShingle Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
Dominic Reeve
Harshinie Karunarathna
title_short Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
title_full Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
title_fullStr Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
title_full_unstemmed Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
title_sort Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting
author_id_str_mv 3e76fcc2bb3cde4ddee2c8edfd2f0082
0d3d327a240d49b53c78e02b7c00e625
author_id_fullname_str_mv 3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Dominic Reeve
0d3d327a240d49b53c78e02b7c00e625_***_Harshinie Karunarathna
author Dominic Reeve
Harshinie Karunarathna
author2 Dominic Reeve
Harshinie Karunarathna
Shunqi Pan
Jose M. Horrillo-Caraballo
Grzegorz Różyński
Roshanka Ranasinghe
format Journal article
container_title Geomorphology
container_volume 256
container_start_page 49
publishDate 2016
institution Swansea University
doi_str_mv 10.1016/j.geomorph.2015.10.016
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description It is now common for coastal planning to anticipate changes anywhere from 70 to 100 years into the future. The process models developed and used for scheme design or for large-scale oceanography are currently inadequate for this task. This has prompted the development of a plethora of alternative methods. Some, such as reduced complexity or hybrid models simplify the governing equations retaining processes that are considered to govern observed morphological behaviour. The computational cost of these models is low and they have proven effective in exploring morphodynamic trends and improving our understanding of mesoscale behaviour. One drawback is that there is no generally agreed set of principles on which to make the simplifying assumptions and predictions can vary considerably between models. An alternative approach is data-driven techniques that are based entirely on analysis and extrapolation of observations. Here, we discuss the application of some of the better known and emerging methods in this category to argue that with the increasing availability of observations from coastal monitoring programmes and the development of more sophisticated statistical analysis techniques data-driven models provide a valuable addition to the armoury of methods available for mesoscale prediction. The continuation of established monitoring programmes is paramount, and those that provide contemporaneous records of the driving forces and the shoreline response are the most valuable in this regard. In the second part of the paper we discuss some recent research that combining some of the hybrid techniques with data analysis methods in order to synthesise a more consistent means of predicting mesoscale coastal morphological evolution. While encouraging in certain applications a universally applicable approach has yet to be found. The route to linking different model types is highlighted as a major challenge and requires further research to establish its viability. We argue that key elements of a successful solution will need to account for dependencies between driving parameters, (such as wave height and tide level), and be able to predict step changes in the configuration of coastal systems.
published_date 2016-03-01T03:29:01Z
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score 11.036706

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