Laboratory water surface elevation estimation using image-based convolutional neural networks
Abstract
With the rapid development of artificial intelligence, estimation of water surface elevation from optical imagery using various deep learning approaches becomes popular. In this study, the convolutional neural network (CNN) is applied to estimate the instantaneous water surface elevation based on the laboratory recorded images. Three typical CNN models, i.e., Baseline, VGGNet and ResNet, are considered and evaluated using four performance metrics (RMSE, R-2, MAE, and WI). All three models generally show satisfactory estimations for the stable water surface elevation, whereas less satisfying estimations for the unstable water surface elevation are confirmed. It is found that water surface elevation estimation is sensitive to the choice of the CNN network structure, while insensitive to the CNN network depth. Subsequently, influence of four hyperparameters is discussed to optimize the CNN performance, and selection strategies are recommended accordingly. Considering the variation of attention map with respect to the epoch and network depth, logics of CNN's learning behavior and process are further specified, upon which the attention hotspots in CNN algorithm are confirmed as the area where significant changes of the image intensity occur owing to the wave propagation. In addition, image rectification can improve the present estimation accuracy of the water surface elevation.