L2. Amenazas por procesos de aguas superficiales
Francisco J.Sáez y Carlos Valle.
Análisis del riesgo y mitigación
Nearshore wave breaking, Optical imaging, Convolutional Neural Networks
Although easily discernible by the naked eye, a robust and consistent methodology to identify the spatio-temporal occurrence of wave breaking in the nearshore on a wave-by-wave basis has been elusive to date. In this work, a Convolutional Neural Network (U-Net) is trained and its performance evaluated using a large number of images in the electro-optical range, and its performance is compared against an existing sensor-fusion methodology. The results show a good performance of the resulting U-Net model, matching nearly 71% of the breaking instances detected by the sensor-fusion approach. Although this value can be seen as low, qualitative comparisons show that in many cases, wave breaking identification is improved by the U-Net model. Moreover, a sample application to a different surfzone showed good qualitative performance, suggesting its applicability to other wave conditions, with short processing times. This offers the possibility of implementing automated wave breaking detection that could enhance our understanding of nearshore processes. The resulting the U-Net model is made available to the community for future testing and continuous development.