Understanding the abundance and distribution of fish in tidal energy streams is important to assess risks presented by introducing tidal energy devices to the habitat. However tidal current flows suitable for tidal energy are often highly turbulent, 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 conventional 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 a deep machine learning model with a U-Net based architecture. Our model, Echofilter, was 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 0.33m on mobile downfacing and 0.5-1.0m 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.

Canadian machine learning researchers from the University of Victoria have teamed up with government marine biologists and private remote sensing specialists to develop a system for improved detection and classification of schools of herring. The world's oceans are home to some 200,000 species of sea animals, including over 18,000 species of fish, more than 1,800 sea stars, 816 squids, 93 whales and dolphins and 8,900 clams and other bivalves, according to a 2015 report from the World Register of Marine Species. Ocean fishes come in a variety of shapes, sizes, and colors and live in many different depth and temperature environments. This diverse marine world is however under threat. A 2016 United Nations Food and Agriculture Organization's World Fisheries and Aquaculture report reveals that 89.5 percent of the world's fish stocks are either fully fished (catches are close to the maximum sustainable yield) or overfished (catches are unsustainable).

Tracking the health of underwater species is critical to understanding the effects of climate change on marine ecosystems. Unfortunately, it's a time-consuming process -- biologists conduct studies with echosounders that use sonar to determine water and object depth, and they manually interpret the resulting 2D echograms. These interpretations are often prone to error and require pricey software like Echoview. Fortunately, a team of research scientists hailing from the University of Victoria in Canada are developing a machine learning method for detecting specific biological targets in acoustic survey data. In a preprint paper ("A Deep Learning based Framework for the Detection of Schools of Herring in Echograms"), they say that their approach -- which they tested on schools of herring -- might measurably improve the accuracy of environmental monitoring.

Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm using hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries.