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One of the most fascinating problems that a computer scientist may be lucky enough to encounter is a complex sociotechnical problem in a field going through the process of digital transformation. For me, that was fish counting. Recently, I worked as a consultant in a subdomain of environmental science focused on counting fish that pass through large hydroelectric dams. Through this overarching project, I learned about ways to coordinate and manage human-in-the-loop dataset production, as well as the complexities and vagaries of how to think about and share progress with stakeholders.
Let’s set the stage. Large hydroelectric dams are subject to Environmental Protection Act regulations through the Federal Energy Regulatory Commission (FERC). FERC is an independent agency of the United States government that regulates the transmission and wholesale sale of electricity across the United States. The commission has jurisdiction over a wide range of electric power activities and is responsible for issuing licenses and permits for the construction and operation of hydroelectric facilities, including dams. These licenses and permits ensure that hydroelectric facilities are safe and reliable, and that they do not have a negative impact on the environment or other stakeholders. In order to obtain a license or permit from FERC, hydroelectric dam operators must submit detailed plans and studies demonstrating that their facility meets regulations. This process typically involves extensive review and consultation with other agencies and stakeholders. If a hydroelectric facility is found to be in violation of any set standards, FERC is responsible for enforcing compliance with all applicable regulations via sanctions, fines, or lease termination–resulting in a loss of the right to generate power.
Hydroelectric dams are essentially giant batteries. They generate power by building up a large reservoir of water on one side and directing that water through turbines in the body of the dam. Typically, a hydroelectric dam requires lots of space to store water on one side of it, which means they tend to be located away from population centers. The conversion process from potential to kinetic energy generates large amounts of electricity, and the amount of pressure and force generated is disruptive to anything that lives in or moves through the waterways—especially fish.
Simple diagram illustrating how hydroelectric power is generated (Tennessee Valley Authority)
It is also worth noting that the waterways were likely disrupted substantially when the dam was built, leading to behavioral or population-level changes in the fish species of the area. This is of great concern to the Pacific Northwest in particular, as hydropower is the predominant power generation means for the region (Bonneville Power Administration). Fish populations are constantly moving upstream and downstream and hydropower dams can act as barriers that block their passage, leading to reduced spawning. In light of the risks to fish, hydropower dams are subject to constraints on the amount of power they can generate and must show that they are not killing fish in large numbers or otherwise disrupting the rhythms of their lives, especially because the native salmonid species of the region are already threatened or endangered (Salmon Status).
To demonstrate compliance with FERC regulations, large hydroelectric dams are required to routinely produce data which shows that their operational activities do not interfere with endangered fish populations in aggregate. Typically, this is done by performing fish passage studies. A fish passage study can be conducted many different ways, but boils down to one primary dataset upon which everything is based: a fish count. Fish are counted as they pass through the hydroelectric dam, using structures like fish ladders to make their way from the reservoir side to the stream side.
A fish ladder at John Day Dam, how fish often ascend and pass through a dam (Delgado)
Fish counts can be conducted visually—-a person trained in fish identification watches the fish pass, incrementing the count as they move upstream. As a fish is counted, observers impart additional classifications outside of species of fish, such as whether there was some kind of obvious illness or injury, if the fish is hatchery-origin or wild, and so on. These differences between fish are subtle and require close monitoring and verification, since the attribute in question (a clipped adipose fin, a scratched midsection) may only be visible briefly when the fish swims by. As such, fish counting is a specialized job that requires expertise in identifying and classifying different species of fish, as well as knowledge of their life stages and other characteristics. The job is physically demanding, as it typically involves working in remote locations away from city centers, and it can be challenging to perform accurately under the difficult environmental conditions found at hydroelectric dams–poor lighting, unregulated temperatures, and other circumstances inhospitable to humans.
These modes of data collection are great, but there are varying degrees of error that could be imparted through their recording. For example, some visual fish counts are documented with pen and paper, leading to incorrect counts through transcription error; or there can be disputes about the classification of a particular species. Different dam operators collect fish counts with varying degrees of granularity (some collect hourly, some daily, some monthly) and seasonality (some collect only during certain migration patterns called “runs”). After collection and validation, organizations correlate this data with operational information produced by the dam in an attempt to see if any activities of the dam have an adverse or beneficial effect on fish populations. Capturing these data piecemeal with different governing standards and levels of detail causes organizations to look for new efficiencies enabled by technology.
Some organizations are exploring the use of computer vision and machine learning to significantly automate fish counting. Since dam operators subject to FERC are required to collect fish passage data anyway, and the data were previously produced or encoded in ways that were challenging to work with, an interesting “human-in-the-loop” machine learning system arises. A human-in-the-loop system combines the judgment and expertise of subject-matter expert humans (fish biologists) with the consistency and reliability of machine learning algorithms, which can help to reduce sources of error and bias in the output dataset used in the machine learning system. For the specific problem of fish counting, this could help to ensure that the system’s decisions are informed by the latest scientific understanding of fish taxonomy and conservation goals, and could provide a more balanced and comprehensive approach to species or morphological classification. An algorithmic system could reduce the need for manual data collection and analysis by automating the process of identifying and classifying species, and could provide more timely and accurate information about species’ health.
Building a computer vision system for a highly-regulated industry, such as hydropower utilities, can be a challenging task due to the need for high accuracy and strict compliance with regulatory standards. The process of building such a system would typically involve several steps:
Representation of example process flow for productionizing a ML system
1. Define the problem space: Before starting to build the system, it is important to clearly define the problem that the system is intended to solve and the goals that it needs to achieve. This initial negotiation process is largely without any defining technical constraints, and is based around the job to that needs to be done by the system: identifying specific tasks that the system needs to perform, such as identification of the species or life stage of a fish. This may be especially challenging in a regulated industry like hydropower, as clients are subject to strict laws and regulations that require them to ensure that any tools or technologies they use are reliable and safe. They may be skeptical of a new machine learning system and may require assurances that it has been thoroughly tested and will not pose any risks to the environment or to through data integrity, algorithmic transparency, and accountability.
Once the problem space is defined, more technical decisions can be made about how to implement the solution. For example, if the goal is to estimate population density during high fish passage using behavioral patterns such as schooling, it may make sense to capture and tag live video, to see the ways in which fish move in real time. Alternatively, if the goal is to identify illness or injury in a situation where there are few fish passing, it may make sense to capture still images and tag subsections of them to train a classifier. In a more developed hypothetical example, perhaps dam operators know that the fish ladder only allows fish to pass through it, all other species or natural debris are filtered out, and they want a “best guess” about rare species of fish that pass upstream. It may be sufficient in this case to implement generic video-based object detection to identify that a fish is moving through a scene, take a picture of it at a certain point, and provide that picture to a human to tag with the species. Once tagged, these data can be used to train a classifier which categorizes fish as being the rare species or not.
2. Establish performance goals: The definition of the problem space and the initial suggested process flow should be shared with all stakeholders as an input to the performance goals. This helps ensure all interested parties understand the problem at a high level, and what is possible for a given implementation. Practically, most hydropower utilities are interested in automated fish count solutions that meet an accuracy threshold of 95% as compared to a regular human visual count, but expectations around whether these metrics are achievable and at what part of the production cycle will be a highly negotiated series of points. Establishing these goals is a true sociotechnical problem, as it cannot be done without taking into account both the real-world constraints that limit the data and the system. These constraining factors will be discussed later in the Obstacles section of the paper.
3. Collect and label training data: In order to train a machine learning model to perform the tasks required by the system, it is first necessary to produce a training dataset. Practically, this involves collecting a large number of fish images. The images are annotated with the appropriate species classification labels by a person with expertise in fish classification. The annotated images are then used to train a machine learning model. Through training, the algorithm learns the features characteristic of each subclass of fish and identifies those features to classify fish in new, unseen images. Because the end goal of this system is to minimize the counts that humans have to do, images with a low “confidence score” (a metric commonly produced by object-detection models) may be flagged for identification and tagging by human reviewers. The more seamless an integration with a production fish counting operation, the better.
4. Select a model: Once the training data has been collected, the next step is to select a suitable machine learning model and train it on the data. This could involve using a supervised learning approach, where the model is trained to recognize the different categories of fish after being shown examples of labeled data. At the time of this writing, deep learning systems based on pretrained models like ImageNet are popular choices. Once trained, the model should be validated against tagged data that it has not seen before and fine-tuned by adjusting the model parameters or refining the training dataset and retraining.
5. Monitor system performance: Once the model has been trained and refined, it can be implemented as part of a computer vision system for regular use. The system’s performance should be monitored regularly to ensure that it is meeting the required accuracy targets and to ensure that model drift does not occur, perhaps from changes in environmental conditions, such as water clarity; or morphological changes alluded to in a later section
It is at this point that the loop of tasks begins anew; to eke out more performance from the system, it is likely that more refined and nuanced negotiation about what to expect from the system is necessary, followed by additional training data, model selection, and parameter tuning/monitoring. The common assumption is that an automated or semiautomatic system like this is “set it and forget it” but the process of curating and collating datasets or tuning hyper parameters is quite engaged and intentional.
In order for the computer vision algorithm to accurately detect and count fish in images or video frames, it must be trained on a large and diverse dataset that includes examples of different fish species and morphologies. However, this approach is not without challenges, as specified in the diagram below and with bolded phrases in subsequent paragraphs:
Recapitulation of diagram above; terminal states specified on the diagram are obstacles to successful system building
Dependence on expert knowledge is a concern worth discussing. If the system relies on expert-tagged data to train and evaluate its algorithms, the system may be vulnerable to errors and biases in the expert’s knowledge and judgments, as any human-in-the-loop system would be. For example, if the experts are not familiar with certain species or morphologies, they may not be able to accurately tag these fish, which could lead to incorrect classifications by the system. Should an invasive species enter the waterway, it may become overrepresented within the dataset and affect the counts of the species that require conservation action. An excellent practical example of this is American shad, of which hundreds of thousands can pass during a migratory period, obscuring the Chinook salmon that are also passing during the same time. Manual counting methods rely solely on the judgment and observation of individual humans, which can be subject to a variety of sources of error and bias. Further, if the experts have a particular interest in certain species or morphologies, they may be more likely to tag these fish, which could result in over- or under-representation within the dataset. This can lead to life-threatening outcomes if the algorithmic system is used to make important decisions that have conservation implications.
Environmental conditions at hydroelectric dams present challenges for data collection as well. Inadequate illumination and poor image quality can make it difficult for both humans and machine learning algorithms to accurately classify fish. Similarly, changing conditions, like a reduction in water clarity following a seasonal snowmelt can obscure fish in imagery. Migratory fish can be difficult to identify and classify on their own terms, due to the wide range of species and subspecies that exist, and the way their bodies change as they age. These fish are often difficult to study and monitor due to their migratory habits and the challenging environments in which they live. Further, there are often inconsistent data taxonomies produced across organizations, leading to different classifications depending on the parent organization undertaking the data tagging process. If humans cannot create accurate classifications to populate the initial dataset, the machine learning system will not be able to accurately produce predictions when used in production.
Example image of rainbow trout from an onsite edge device; challenging to tell from the lighting but those could be natural spots, injury, or parasitic infection
One of the key challenges of using a machine learning classifier on unaudited data is the risk of model drift, in which the model’s performance degrades over time as the underlying data distribution changes. This may be of particular concern in a highly regulated environment, where even small changes in the model’s performance could have significant consequences. The datasets produced through the effort of tagging fish images are fascinating because they are so intrinsically place-based, situated, and not easily replicable. Fish passage studies often involve monitoring a relatively small number of fish, which can make it difficult to accurately assess the overall profile of fish populations in the wider area. The number and types of fish that pass through a dam’s fish ladders or other fish passage structures can vary greatly depending on the time of year or the “run” of fish passing through the waterways. This can make it difficult to compare data from different studies, or to draw conclusions about the long-term impact of the dam on fish populations. If the system is trained on a dataset of fish that has been tagged by subject-matter experts during one season, the dataset may not be comprehensive or representative of the full range of fish species and morphologies that exist in the wild across the full year. This could lead to under- or over-estimations of number and types of fish present in a given area. In this way, the specter of model drift is actually a problem composed of both challenging data production constraints and dependence on expert knowledge.
Finally, there are background labor issues to be dealt with as part of this problem space coming from intense organizational pressure. Fish counting is a cost center that hydroelectric dam operators would like to eliminate or reduce as much as possible. A technical solution that can accurately count fish is therefore very appealing. However, this raises concerns about ghost work, where human labor is used to train and validate the model, but is not acknowledged or compensated. Replacing human workers with a computer vision solution may significantly impact the displaced workers through financial hardship or the obsoletion of their job skills and expertise. If human expertise in the identification of fish is lost, this could lead to suboptimal decisions about species conservation, and could ultimately undermine the effectiveness of the system. This becomes more dangerous for conservation purposes if the technology is implemented as a cost-reduction measure: it could be the case that—when the model drifts—there are no taggers to set it back on track.
Couple all of these points with the longitudinal decline of wild fish populations globally, and you have a challenging set of conditions to attempt to generalize from.
If the available training data is limited or does not accurately reflect the diversity of fish species and morphologies that pass through the dam’s fish passage structures, the accuracy of the algorithm may be reduced. Additionally, there are concerns about data leakage, where the model may be able to infer sensitive information about the fish from the images, such as how they are routed through the dam. Thinking about studies that happen in fisheries as per Hwang (2022), the populations analyzed are so small and the outcomes so intentionally so narrowly-scoped, it is almost the case that an organization would have to at the very least train a one-off model for each project or validate the output of each ML classifier against some additional source, which is lately outside of the interest and capabilities of organizations who hope to reduce labor outlays as part of the implementation of a system like this.
The sociotechnical problem of fish counting is a niche problem with wide applications. If properly implemented, a machine learning system based around fish counts has the potential to be applied in many different places, such as meeting environmental regulation or aquaculture. The rapid digital transformation of environmental science has led to the development of novel datasets with interesting challenges, and a new cohort of professionals with the data literacy and technical abilities to work on problems like this. However, building a dataset of anadromous and catadromous fish that are protected under the ESA is a complex and challenging task, due to the limited availability of data, the complexity of fish taxonomy, the involvement of multiple stakeholders, and the dynamic environment in which these species live.
Moreover, organizations subject to regulation may be unsure of how to validate the accuracy of a machine learning model, and may be more interested in fish counts than in fish images (or vice-versa). Bringing new technologies to bear on an organization or on a dataset that was not as robustly cataloged means there will be new things to be discovered or measured through the application of the technology. Since implementation of a computer vision system like this is done to meet compliance with FERC regulations, it means bringing multiple different stakeholders–including federal agencies, state and local governments, conservation organizations, and members of the public–into dialogue with one another when changes are required. By conducting these studies and regularly reporting the results to FERC, a hydroelectric dam operator could demonstrate that they are taking steps to minimize the impact of the dam on fish populations, and that the dam is not having a negative impact on the overall health of the local fish population, but it also means cross-checking with the community in which they are situated.
Kevin McCraney is a data engineer, educator, and consultant. He works with public sector & large-scale institutions building data processing infrastructure & improving data literacy. Kevin has several years of experience teaching & mentoring early career professionals as they transition to technology from non-STEM disciplines. Working predominantly with institutions in the Pacific Northwest, he enjoys professional opportunities where he can combine a humanistic worldview and technical acumen to solve complex sociotechnical problems.
For attribution of this in academic contexts or books, please cite this work as:
Kevin McCraney, “Salmon in the Loop“, The Gradient, 2023.
@articlek2023omccraney,
author = McCraney, Kevin,
title = Salmon in the Loop,
journal = The Gradient,
year = 2023,
howpublished = urlhttps://thegradient.pub/salmon-in-the-loop,
[1]Bonneville Power Administration. (n.d.). Hydropower impact. Hydropower Impact. Retrieved January 14, 2023, from https://www.bpa.gov/energy-and-services/power/hydropower-impact
[2]Delgado, K. (2021, July 19). That sounds fishy: Fish ladders at high-head dams impractical, largely unneeded. www.army.mil. Retrieved January 3, 2023, from https://www.army.mil/article/248558/that_sounds_fishy_fish_ladders_at_high_head_dams_impractical_largely_unneeded
[3]Hwang, I. (2022, May 31). Salmon hatchery data is harder to handle than you think. ProPublica. Retrieved December 10, 2023, from https://www.propublica.org/article/salmon-hatcheries-pnw-fish-data
[4]Salmon status. State of Salmon. (2021, January 11). Retrieved December 29, 2022, from https://stateofsalmon.wa.gov/executive-summary/salmon-status/
[5]How hydroelectric power works. Tennessee Valley Authority. (2021, January 11). Retrieved December 29, 2022, from https://www.tva.com/energy/our-power-system/hydroelectric/how-hydroelectric-power-works
