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

Dominic E. Reeve, Harshinie Karunarathna, Shunqi Pan, Jose M. Horrillo-Caraballo, Grzegorz Rozynski, Roshanka Ranasinghe

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Scopus)
68 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)49-67
Publication statusPublished - 2016


  • METIS-314426
  • IR-100689


Dive into the research topics of 'Data-driven and hybrid coastal morphological prediction methods for mesoscale forecasting'. Together they form a unique fingerprint.

Cite this