Aiming at the problem of calculating the overtopping of single-slope breakwaters, a mean impact value-backpropagation (MIV-BP) estimation model for predicting overtopping was established. Experimental data from the Tianjin Research Institute of Water Transport Engineering (TIWTE) were utilized to further enrich the dataset of the CLASH project for single-slope wave overtopping discharge. This paper established a comprehensive prediction model based on an ensemble learning average method combination strategy. There are 10 input parameters in the model, including the offshore effective wave height, average wave period, offshore water depth, toe submergence, toe width, slope tangent, armor rock surface roughness factor, crest height with respect to the static water level, wall height with respect to the static water level, and crest width; the output parameter is the mean overtopping discharge. Subsequently, a comparative analysis was conducted between this estimation model, the Chinese standard formula calculation model, and the European Van der Meer formula calculation model. Compared with the two formulas mentioned above, this estimation model’s coefficient of correlation increased by 0.23 and 0.26, respectively. Finally, a weight evaluation analysis of the 10 main factors affecting overtopping was carried out based on a MIV-BP neural network model. In the analysis, a positive correlation was found for factors, such as the wave height, average wave period, and water depth at the structure toe; a negative correlation was found for factors, such as the slope, crest height with respect to the static water level, wall height with respect to the static water level, and crest width. Overall, the results provide a significant basis and reference for optimizing the design of the wave overtopping control.

This study proposed a spatial wave measurement method based on a U-net convolutional neural network. First, frame images are extracted from a video collected by a physical model experiment, and a dataset of spatial wave measurements is created and extended using a data enhancement method. A U-net convolutional neural network is built to extract the spatial wave information of the images; evidently, the segmented water level is close to that of the original image. Next, the U-net convolutional neural network is compared with the sensor, pixel recognition, and Canny edge detection methods. Pixel recognition results reveal that the maximum and minimum errors of the U-net convolutional neural network are 3.92% and 1.05%, those of the Canny edge detection are 5.97% and 1.33%, and those of the sensor are 11.8% and 1.6%, respectively. Finally, the nonlinear characteristic quantities of waves are measured using the proposed U-net convolutional neural network. The kurtosis and asymmetry calculated in the spatial domain are slightly larger than those calculated in the time domain, whereas the skewness calculated in the spatial domain is smaller than that calculated in the time domain. The asymmetry and kurtosis increase with an increase in wave height and period, whereas the skewness increases with an increase in wave height but decreases with an increase in period.