@article{fdi:010091883,
  title = {{P}redicting beach profiles with machine learning from offshore wave reflection spectra},
  author = {{D}isdier, {E}. and {A}lmar, {R}afa{\¨e}l and {B}enshila, {R}. and {A}l {N}ajar, {M}. and {C}hassagne, {R}. and {M}ukherjee, {D}. and {W}ilson, {D}. {G}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}racking and forecasting changes in coastal morphology is vital for development, risk reduction, and overall coastal management. {O}ne challenge of current coastal research and engineering is to find a method able to accurately assess the bathymetry profile along the coast and key parameters such as slope and sandbars. {T}raditional bathymetry measurements are obtained through echo-sounding, which is time-consuming, hazardous and costly. {U}sing a variety of simulated cases, we test the potential of machine learning and in particular {N}eural {N}etworks to reconstruct the coastal bathymetry profile from offshore sensed waves, based on shore-based wave reflection. {F}eatures such as foreshore slope, curvature, sandbars amplitude and positions can be captured.},
  keywords = {{M}achine learning ; {ANN} ; {B}athymetry ; {S}and bars ; {B}each slope ; {W}ave reflection},
  booktitle = {},
  journal = {{E}nvironmental {M}odelling and {S}oftware},
  volume = {183},
  numero = {},
  pages = {106221 [11 p.]},
  ISSN = {1364-8152},
  year = {2025},
  DOI = {10.1016/j.envsoft.2024.106221},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091883},
}