Coast and ocean collective
Home
Research
  • Hydrodynamics
  • Morphodynamics
  • Coastal Hazards
Resources
  • Videos, Talks & Stuff
  • Data
  • Books
  • Educational Kids Videos
  • Coast2Coast
  • Publications
People
Coast2Cast
COMOMoLA
Coast and ocean collective
Home
Research
  • Hydrodynamics
  • Morphodynamics
  • Coastal Hazards
Resources
  • Videos, Talks & Stuff
  • Data
  • Books
  • Educational Kids Videos
  • Coast2Coast
  • Publications
People
Coast2Cast
COMOMoLA
More
  • Home
  • Research
    • Hydrodynamics
    • Morphodynamics
    • Coastal Hazards
  • Resources
    • Videos, Talks & Stuff
    • Data
    • Books
    • Educational Kids Videos
    • Coast2Coast
    • Publications
  • People
  • Coast2Cast
  • COMOMoLA
  • Home
  • Research
    • Hydrodynamics
    • Morphodynamics
    • Coastal Hazards
  • Resources
    • Videos, Talks & Stuff
    • Data
    • Books
    • Educational Kids Videos
    • Coast2Coast
    • Publications
  • People
  • Coast2Cast
  • COMOMoLA

COastal MOrphodynamics and Machine LeArning: Building an Inclusive International Network of Scientists

 

This global initiative aims to establish an international network of scientists passionate about coastal processes and the application of machine learning techniques. The objective is to foster knowledge exchange, collaboration, and innovation in understanding and predicting coastal processes using cutting-edge data-driven approaches. By organizing online seminars, researchers from around the world can share their expertise, present their findings, and engage in interdisciplinary discussions. This network seeks to be inclusive, welcoming scientists from diverse backgrounds and expertise levels, ensuring a broad range of perspectives. Through this collaborative platform, participants can forge connections, build partnerships, and collectively contribute to addressing the challenges of coastal morphodynamics. By exploring the power of machine learning and promoting open collaboration, this network strives to enhance predictive capability, advance knowledge and promote novel modelling strategies.  

COMO MoLA was initiated by Kristen Splinter (University of New South Wales), Harshinie Karunarathna (Swansea University), Daniel Buscombe (USGS), Evan Goldstein (UNC Greensboro), Nadia Senechal (University of Bordeaux), Sean Vitousek (USGS) and Giovanni Coco (University of Auckland).


Since this is a global network, the zoominars will happen at the speaker’s preferred time with the recording posted on this page asap. (if a video doesn't play, contact g.coco@auckland.ac.nz)


Join in using the form at the bottom of this page   

Upcoming Zoominars

Freya Muir (University of Glasgow )

   Real-time monitoring and modelling of coastal change: a PhD combining satellites and deep learning across the full shoreface

 May 29, 2025 09:00 AM London 

Past Zoominars

Mika Siegelman - 30 April 2025

Subaerial Profiles at Two Beaches: Equilibrium and Machine Learning.



Useful reference:  

Siegelman, M.N., McCarthy, R.A.,  Young, A.P., O’Reilly, W., Matsumoto, H., Johnson, M., Mack, C. and  Guza, R.T., 2024. Subaerial profiles at two beaches: Equilibrium and  machine learning. Journal of Geophysical Research: Earth Surface, 129(10), p.e2023JF007524.

Michael Hasson - 14 February 2025

Automated determination of transport and depositional environments in sand and sandstones


  Github repository: https://github.com/michaelhasson/SandAI 


Useful reference: M. Hasson, M. C. Marvin, M. G. A. Lapôtre, Automated determination of transport and depositional environments in sand and sandstones. Proc. Natl. Acad. Sci. U.S.A. 121, e2407655121, 2024.

Reda Snaiki - 1 October 2024

Rapid Assessment of Wave and Storm Surge Responses Over an Extended Coastal Region using a Novel Hybrid Machine Learning Model  


Useful references: Naeini, S.S. and Snaiki, R., 2024. A novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region. Coastal Engineering, 190, p.104503.

Salika Thilakarathne - 19 August 2024

Explainable machine learning with storm-induced beach erosion


Useful references: Thilakarathne S, Suzuki T, Mäll M.  Machine learning-driven approach to quantify the beach susceptibility to  storm-induced erosion. Coastal Engineering Journal. 2024 Apr  2;66(2):216-33.

Nicoletta Leonardi - 30 July 2024

 Changing Coastlines: insights from modelling and machine learning 


Useful references: Kumar, Pavitra, and Nicoletta Leonardi. "Exploring mega‐nourishment interventions using Long Short‐Term Memory (LSTM) models and the sand engine surface MATLAB framework." Geophysical Research Letters 51, no. 4 (2024): e2023GL106042. 

Orencio Duran Vinent- 14 May 2024

Application of CNN-Based Image Segmentation for Tracking Coastal Flooding and Post-Storm Recovery


 Useful references: Kang, B., Feagin, R.A., Huff, T. and  Durán Vinent, O., 2024. Stochastic properties of coastal flooding  events–Part 1: convolutional-neural-network-based semantic segmentation  for water detection. Earth Surface Dynamics, 12(1), pp.1-10. 

Michael Thompson - 19 March 2024

 The versatility of Convolutional Neural Network's for coastal monitoring applications


 Useful references: Useful references: Thompson, M., Zelich, I., Watterson,  E. and Baldock, T.E., 2021. Wave peel tracking: A new approach for  assessing surf amenity and analysis of breaking waves. Remote Sensing, 13(17), p.3372.

Harshinie Karunarathna & Kristian Ions - 15 February 2024

 Artificial Intelligence applications to modeling coastal change 

  

 Useful references: Karunarathna, H. and Reeve, D.E. (2013) A hybrid approach to model shoreline change at multiple timescales, Continental Shelf Research. 66, pp.29-35. doi.org/10.1016/j.csr.2013.06.019.

Daniel Buscombe - 30 November 2023

Multi-year monitoring of large wood and sediment in a coastal mountain river and its delta using deep learning   


 Useful references: Warrick, J.A., Stevens, A.W., Miller, I.M., Harrison, S.R., Ritchie, A.C. and Gelfenbaum, G., 2019. World’s largest dam removal reverses coastal erosion. Scientific Reports, 9(1), p.13968.

K. Splinter & G.Coco - 23 October 2023

Coastal Morphodynamics in the Age of Machine Learning


 Useful references: Gomez-de la Peña, E., Coco, G., Whittaker, C. and Montano, J., 2023. On the use of Convolutional Deep Learning to predict shoreline change. Earth Surf. Dyn., 11, 1145–1160, https://doi.org/10.5194/esurf-11-1145-2023  

Join in!

Please click on the link below to receive and email about upcoming COMO MoLA zoominars. 

You can also use the form to let us know if you want to give a talk!

 https://forms.office.com/Pages/ResponsePage.aspx?id=pM_2PxXn20i44Qhnufn7o0iD4P5C4N5PvHxDzB0QgAdUREFWOTQzS0VMUUkwMTJJVzE1RkgyWFhEUy4u


Copyright © 2019 Coast and Ocean Collective - All Rights Reserved.

coastandoceancollective@gmail.com 

Powered by