Understanding the abundance and distribution of fish in tidal energy streams is important for assessing the risks presented by the introduction of tidal energy devices into the habitat. However, tidal current flows suitable for tidal energy development are often highly turbulent and entrain air into the water, complicating the interpretation of echosounder data. The portion of the water column contaminated by returns from entrained air must be excluded from data used for biological analyses. Application of a single algorithm to identify the depth-of-penetration of entrained air is insufficient for a boundary that is discontinuous, depth-dynamic, porous, and varies with tidal flow speed.

Using a case study at a tidal energy demonstration site in the Bay of Fundy, we describe the development and application of deep machine learning models with a U-Net based architecture that produce a pronounced and substantial improvement in the automated detection of the extent to which entrained air has penetrated the water column.

Our model, Echofilter, was found to be highly responsive to the dynamic range of turbulence conditions and sensitive to the fine-scale nuances in the boundary position, producing an entrained-air boundary line with an average error of 0.33 m on mobile downfacing and 0.5–1.0 m on stationary upfacing data, less than half that of existing algorithmic solutions. The model’s overall annotations had a high level of agreement with the human segmentation, with an intersection-over-union score of 99% for mobile downfacing recordings and 92–95% for stationary upfacing recordings. This resulted in a 50% reduction in the time required for manual edits when compared to the time required to manually edit the line placement produced by the currently available algorithms. Because of the improved initial automated placement, the implementation of the models permits an increase in the standardization and repeatability of line placement.

了解潮汐能流中鱼类的丰度和分布对于评估将潮汐能设备引入栖息地所带来的风险非常重要。然而，适合潮汐能开发的潮汐流通常是高度湍流的，并将空气夹带入水中，使回声测深仪数据的解释复杂化。被夹带的空气回流污染的水柱部分必须从用于生物分析的数据中排除。对于不连续、深度动态、多孔且随潮汐流速变化的边界，应用单一算法来识别夹带空气的渗透深度是不够的。

使用芬迪湾潮汐能示范站点的案例研究，我们描述了具有基于 U-Net 架构的深度机器学习模型的开发和应用，该模型在自动检测的程度方面产生了显着和实质性的改进。夹带的空气已渗入水柱。

我们的模型 Echofilter 被发现对湍流条件的动态范围具有高度响应性，并且对边界位置的细微差别敏感，产生的夹带空气边界线在移动式下沉时的平均误差为 0.33 m，平均误差为 0.5 –1.0 m 在固定正面数据上，不到现有算法解决方案的一半。该模型的整体注释与人类分割高度一致，移动向下记录的交叉联合得分为 99%，静止向上记录的交叉联合得分为 92-95%。与手动编辑当前可用算法产生的行放置所需的时间相比，这导致手动编辑所需的时间减少了 50%。由于改进了初始自动放置，