Diverse algae form algae canopies and turfs with various community structures, which play an important ecological role in coastal waters. Acoustic methods have been suggested and applied as effective quantitative methods for some algae canopy measurements across a large-scale area. However, these approaches face difficulties in accurately classifying turfs from barren seafloor due to weak backscattering strength. Thus, to estimate the community structure of various algae assemblages, we developed a classification method using a combination of acoustic-derived physical distance and backscattering strength parameters using a scientific echosounder. The prediction accuracy for algae or barren seafloor using four machine learning methods based on seven parameters was higher than that for the manual classification results based only on the acoustic physical distance. The classification accuracies of six types of algae canopy, turf, and barren seafloor were also higher than those obtained based only on commonly used seafloor parameters. Hence, machine learning methods based on the seven derived parameters from acoustic data are suggested to be effective for the classification. Applications in the classification and distribution estimations of various types of algae canopies, turfs, and potential algae habitat areas are promising.

多样的藻类形成具有不同群落结构的藻类冠层和草皮，在沿海水域中起着重要的生态作用。已经提出了声学方法，并将其作为有效的定量方法，用于在大范围内进行某些藻类冠层测量。然而，由于弱的反向散射强度，这些方法在从贫瘠的海底准确地对草皮进行准确分类方面面临困难。因此，为了估算各种藻类群落的群落结构，我们使用了科学回声法，结合了声源物理距离和后向散射强度参数，开发了一种分类方法。使用基于七个参数的四种机器学习方法对藻类或贫瘠海底的预测准确性要高于仅基于声学物理距离的人工分类结果的预测准确性。六种藻类冠层，草皮和贫瘠海底的分类精度也高于仅基于常用海底参数获得的分类精度。因此，建议基于从声学数据中导出的七个参数的机器学习方法对于分类是有效的。在各种类型的藻冠层，草皮和潜在藻类栖息地区域的分类和分布估计中的应用前景广阔。和贫瘠海底也比仅基于常用海底参数获得的高。因此，建议基于从声学数据中导出的七个参数的机器学习方法对于分类是有效的。在各种类型的藻冠层，草皮和潜在藻类栖息地区域的分类和分布估计中的应用前景广阔。和贫瘠海底也比仅基于常用海底参数获得的高。因此，建议基于从声学数据中导出的七个参数的机器学习方法对于分类是有效的。在各种类型的藻冠层，草皮和潜在藻类栖息地区域的分类和分布估计中的应用前景广阔。