An Electric Power Research Institute and Pacific Northwest National Laboratory (PNNL) team optimized advanced machine learning technology that uses sonar data to identify migrating eels near hydropower facilities. The resulting object detection software uses images and videos to identify multiple fish species and distinguish them in near-real time from other objects in the water.  

Existing technology at hydropower facilities cannot automatically detect migrating eels, so many operators curtail turbine operation during migration season to ensure safe eel passage. This new technology will help operators better identify these migrating eels, decreasing injuries to these fish populations and reducing the need to cut back turbine operation when fish are not present. 

The team—which included machine learning experts, data scientists, biologists, and engineers—experimented with and optimized data and information (such as neural networks, algorithms, training datasets, and thresholding specifications) to ensure computers could identify the correct fish shapes from field data collected by the Electric Power Research Institute’s Eel Research Centre. The team further tested the detection software at PNNL’s Aquatic Research Laboratory with fish to validate and improve the program’s performance.