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

Automated determination of transport and depositional environments in sand and sandstones

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

Useful references: 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.

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.

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.

 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. 

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. 

 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.

 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.

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.

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