Optical nearshore wave gauging with deep neural networks
We describe a new technique called 'optical wave gauging' or OWG that is designed to be configurable for estimating various quantities of nearshore waves, from single or multiple images taken at arbitrary viewpoints, with no information about camera geometry or image coordinate transformations required. OWG is currently designed for basic monitoring of coastal hydrodynamic quantities such as wave height, period, direction, tidal elevation, etc.
OWG is a model framework based on deep convolutional neural networks that can be purposed differently for each task; single image-single output (such as wave height or period); multiple images-single outputs; or multiple images-multiple outputs. We demonstrate the OWG model framework by configuring it to estimate time-series of various wave height and period quantities from a two year time-series of static images from one or two cameras from a USGS coastal video monitoring system at Sunset State Beach, California, USA. We examine model and model training sensitivities and discuss requirements for adapting the technique for operational nearshore wave monitoring from cameras alone. Finally, we discuss the feasibility of ‘real-time’ monitoring using single-board computers in the field.