Beach profiles vary significantly based on sediment supply and hydrodynamic, aeolian, and anthropogenic influences. Successful management of beach and dune resources is heavily based on understanding and classifying beach profile shapes based on their dominant features. As such, the highly dynamic shape of beach profiles has motivated researchers to classify them based on different criteria where the number of groups depend on the empirical basis and actual coastal environment. In the interest of classification, beside the theoretical and analytical approaches, nowadays, the application of artificial intelligence, especially, deep learning is well recognized. To this end, in this study, subaerial beach profiles are classified through unsupervised learning and cluster analysis. Accordingly, a convolutional neural network is designed that can search the entire dataset and categorizes beach profiles without any prior definitions. With the proposed method, 916 subaerial beach profiles from 2005 to 2018, representing six beaches from three different towns in Maine, USA were categorized into 2, 3, and 5 categories. The results showed that depending on the number of required categories, the proposed model was able to spot the most common features among all profiles in a category.

海滩剖面因沉积物供应和水动力、风沙和人为影响而显着不同。海滩和沙丘资源的成功管理很大程度上基于对海滩剖面形状的了解和根据其主要特征进行分类。因此，海滩剖面的高度动态形状促使研究人员根据不同的标准对它们进行分类，其中组的数量取决于经验基础和实际沿海环境。为了分类，除了理论和分析方法外，如今人工智能的应用，特别是深度学习的应用也得到了广泛认可。为此，在本研究中，通过无监督学习和聚类分析对空中海滩剖面进行了分类。因此，设计了一个卷积神经网络，可以搜索整个数据集并对海滩轮廓进行分类，而无需任何事先定义。使用所提出的方法，从 2005 年到 2018 年，代表来自美国缅因州三个不同城镇的六个海滩的 916 个海底海滩剖面被分为 2、3 和 5 类。结果表明，根据所需类别的数量，所提出的模型能够发现类别中所有配置文件中最常见的特征。