Machine learning (ML) classifiers provide convenience and accuracy in coastline extraction compared to traditional methods and image processing techniques. In literature, the studies about coastline extraction with machine learning classifiers are not focused adequately on the coast types that affect the process. To eliminate this gap, machine learning classifiers were examined in terms of their accuracies on different coastal morphologies. ML classifiers were divided into 3 main groups: Support Vector Machines (SVMs), Multi-Layer Perceptron (MLP) and Ensemble Learning (EL) Classifiers. Within the groups, coastlines were estimated by utilizing different formulas and/or classifiers and their accuracies were examined considering different coast types. Most frequently encountered coastal types, including bedrock, beaches and artificial coasts are included in the study. Bedrock and beach type of coasts were investigated by dividing into sub-groups as shaded, unshaded bedrock coasts and silty-sandy, sandy-gravel beaches. Classifiers were observed as accurate on unshaded bedrock coasts and their results were similar. In spite of that, extraction errors were incurred on the bedrock coasts due to shadows. MLP classifiers with Linear, Logarithmic, and Tanh activation functions were the most accurate in these areas. The challenge was shallow depths and suspended solids in beach type coasts. EL classifiers and SVMs with sigmoidal kernel function were adversely affected on these areas whilst the best results were obtained by utilizing the other SVMs and MLP classifiers. On artificial coasts, successful results were obtained with all classifiers.

与传统方法和图像处理技术相比，机器学习 (ML) 分类器在海岸线提取方面提供了便利性和准确性。在文献中，关于使用机器学习分类器提取海岸线的研究并未充分关注影响该过程的海岸类型。为了消除这一差距，机器学习分类器在不同海岸形态的准确性方面进行了检查。ML 分类器分为 3 个主要组：支持向量机 (SVM)、多层感知器 (MLP) 和集成学习 (EL) 分类器。在这些组内，通过使用不同的公式和/或分类器来估计海岸线，并考虑不同的海岸类型检查它们的准确性。最常见的沿海类型，包括基岩，研究中包括海滩和人工海岸。基岩和海滩类型的海岸被划分为有阴影、无阴影的基岩海岸和粉砂、砂砾石海滩。分类器在无阴影的基岩海岸上被观察到是准确的，它们的结果是相似的。尽管如此，由于阴影，在基岩海岸发生了提取错误。具有线性、对数和 Tanh 激活函数的 MLP 分类器在这些领域中是最准确的。挑战在于海滩型海岸的浅水深度和悬浮固体。EL 分类器和具有 S 型核函数的 SVM 在这些区域受到不利影响，而使用其他 SVM 和 MLP 分类器获得了最佳结果。在人工海岸上，所有分类器都获得了成功的结果。基岩和海滩类型的海岸被划分为有阴影、无阴影的基岩海岸和粉砂、砂砾石海滩。分类器在无阴影的基岩海岸上被观察到是准确的，它们的结果是相似的。尽管如此，由于阴影，在基岩海岸发生了提取错误。具有线性、对数和 Tanh 激活函数的 MLP 分类器在这些领域中是最准确的。挑战在于海滩型海岸的浅水深度和悬浮固体。EL 分类器和具有 S 型核函数的 SVM 在这些区域受到不利影响，而使用其他 SVM 和 MLP 分类器获得了最佳结果。在人工海岸上，所有分类器都获得了成功的结果。基岩和海滩类型的海岸被划分为有阴影、无阴影的基岩海岸和粉砂、砂砾石海滩。分类器在无阴影的基岩海岸上被观察到是准确的，它们的结果是相似的。尽管如此，由于阴影，在基岩海岸发生了提取错误。具有线性、对数和 Tanh 激活函数的 MLP 分类器在这些领域中是最准确的。挑战在于海滩型海岸的浅水深度和悬浮固体。EL 分类器和具有 S 型核函数的 SVM 在这些区域受到不利影响，而使用其他 SVM 和 MLP 分类器获得了最佳结果。在人工海岸上，所有分类器都获得了成功的结果。