Room: 5-314
Speaker Name:
Ian Robertson
Affiliation:
Abstract:
Understanding the breaking characteristics of waves is important in several nearshore applications such as assessing impacts of engineered structures on wave breaking or computing surf zone energy budgets. Past studies have used images collected by remote sensing to estimate characteristics such as breaking wave height, depth, position, and type (e.g., plunging, spilling, non-breaking). Due to the dynamic nature of breaking waves, breaker classification from a single image is difficult; an approach involving multiple frames is likely more effective. Here, we develop a You Only Look Once – Random Forest (YOLO-RF) machine learning (ML) model to predict breaker type (plunging or spilling) from GoPro video data shot cross-shore at oncoming waves (face-on) in a large wave flume. A YOLO model which classifies five wave features (e.g., prebreaking, curling, splashing, whitewash, crumbling) in a set of video frames (images) is coupled to an RF model which takes normalized feature counts (over multiple frames) as inputs, outputting a wave-breaking type for each detected wave. The YOLO model detects wave features as separate objects, allowing for individual classification of waves in the same frame. This approach identifies breaker type with 94% accuracy.
We applied this method to ongoing research in designing a hybrid engineered reef, showing that thin-walled, porous structures shift the dominant wave breaking class from plunging to spilling. The trained ML model was useful for rapidly labeling relatively consistent laboratory data. A similar approach could be implemented in field settings to aid in understanding, predicting, and modeling wave breaking dynamics in the nearshore environment.