AI Tool Predicts Whale Movements to Reduce Ship Strikes and Enhance Ocean Conservation

AI predicts whale movements to reduce deadly ship strikes - Earth.com

An artificial intelligence (AI) tool has been developed to predict the habitat of endangered whales, helping to reduce deadly ship strikes and promote responsible ocean development. The tool was designed by researchers at Rutgers University-New Brunswick. By analyzing vast datasets, the AI model improves existing monitoring techniques, offering a more precise way to track the movements of the critically endangered North Atlantic right whale. This species has been listed as endangered under the Endangered Species Act since 1970. Currently, the whales have an estimated population of only 370 individuals, including around 70 reproductively active females, according to the U.S. National Oceanic and Atmospheric Administration (NOAA). The project was led by Ahmed Aziz Ezzat, an assistant professor in the Department of Industrial and Systems Engineering at Rutgers' School of Engineering. Ezzat collaborated with Josh Kohut, a Rutgers professor of marine sciences, and doctoral student Jiaxiang Ji, who is the study's first author. Professor Kohut likened the AI model's function to tracking human movement patterns in a household, where factors like food in the kitchen or a television in the den influence where people gather at specific times. "With this program, we're correlating the position of a whale in the ocean with environmental conditions," Kohut explained. "This allows us to become much more informed on decision making about where the whales might be." "We can predict the time and location that represents a higher probability for whales to be around. This will enable us to implement different mitigation strategies to protect them." The researchers initially aimed to create high-resolution habitat models to support offshore wind farm development. However, their findings have broader implications, prompting them to publicly share additional details as part of their study. "These tools are valuable and would solidly benefit anyone engaged in the blue economy - including fishing, shipping and developing alternative forms of energy sustainably," said Ezzat. "This approach can support a wise and environmentally responsible use of these waters so that we achieve our economic objectives, and at the same time make sure that we cause minimal to no harm to the environmental habitat of these creatures." Unlike traditional programs that operate on fixed instructions, the machine-learning model used in this study analyzed massive datasets to detect patterns and refine its predictions over time. "The outcome of the machine-learning model is basically a prediction of where and when you will have a higher likelihood of encountering a marine mammal," Ezzat said, describing it as a "probability map." The model integrated data from the Rutgers University Center for Ocean Observing Leadership, which has been gathering whale detection and oceanographic data since 1992. The analysis also incorporated satellite data from the University of Delaware. To build an accurate predictive tool, the researchers used two primary sources of oceanographic data: autonomous underwater gliders and satellite-based measurements. Underwater gliders, torpedo-shaped robotic vessels, collect real-time information on seawater temperature, salinity, currents, and chlorophyll levels. They also use sonar to assess fish populations and record whale vocalizations, allowing researchers to pinpoint their locations. Meanwhile, satellite data provides broader environmental context, including sea surface temperature, water color, and oceanic fronts -- factors that influence whale distribution. "We've had the data but, until now, we've not been able to put the two sets - those detections of where the whales are, and what the environment is like at those places - together," Kohut said. "This is a demonstration of the power of employing AI methodologies to advance our ability to predict or estimate where these whales are." By harnessing AI to integrate decades of environmental and whale tracking data, the Rutgers team has developed a powerful tool to improve conservation efforts, mitigate ship collisions, and support sustainable ocean development. As human activities increasingly intersect with marine ecosystems, this AI-driven approach provides a critical step toward protecting North Atlantic right whales and ensuring responsible management of ocean resources. Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.

Scientists Harness AI to Help Protect Whales, Advancing Ocean Conservation | Newswise

Researchers at Rutgers University-New Brunswick have developed an artificial intelligence (AI) tool that will help predict endangered whale habitat, guiding ships along the Atlantic coast to avoid them. The tool is designed to prevent deadly accidents and inform conservation strategies and responsible ocean development. Using an AI-powered computer program that learns from patterns detected between two vast databases, the researchers said their method improved upon present abilities to monitor the ocean for the distribution of important marine species, such as the critically endangered North Atlantic right whale. North Atlantic right whales have been listed as endangered under the Endangered Species Act since 1970. There are approximately 370 individuals remaining, including about 70 reproductively active females, according to the U.S. National Oceanic and Atmospheric Administration. The researchers' report was published in Nature Scientific Reports. The effort was led by Ahmed Aziz Ezzat, an assistant professor in the Department of Industrial and Systems Engineering at the School of Engineering, and Josh Kohut, a professor in marine sciences who in January became dean of research at the School of Environmental and Biological Sciences. Ezzat leads a research group on applied machine learning for engineering and physical sciences. Jiaxiang Ji, the paper's first author and a doctoral student in the School of Engineering, contributed significantly to the project. Kohut likened the output of the program to what might be learned by tracking the movements of people in a house as well as determining whether there is food in the kitchen and a television set on in the den. Such factors might determine why people are where they are at certain times of the day. Detecting certain patterns, he said, conveys predictive power. "With this program, we're correlating the position of a whale in the ocean with environmental conditions," Kohut said. "This allows us to become much more informed on decision making about where the whales might be. We can predict the time and location that represents a higher probability for whales to be around. This will enable us to implement different mitigation strategies to protect them." Initially, the researchers sought to develop high-resolution models of the North Atlantic right whale presence to support responsible offshore wind farm development and operation. But they said the results have far broader implications and have made the details public as an addendum to their research paper. "These tools are valuable and would solidly benefit anyone engaged in the blue economy - including fishing, shipping and developing alternative forms of energy sustainably," Ezzat said. "This approach can support a wise and environmentally responsible use of these waters so that we achieve our economic objectives, and at the same time make sure that we cause minimal to no harm to the environmental habitat of these creatures." Unlike typical computer programs, where instructions are explicitly written out, the machine-learning program employed by the researchers analyzed large data sets to discover patterns and relationships. As the AI program encountered more data, it adjusted its internal model to make better predictions or classifications. "The outcome of the machine-learning model is basically a prediction of where and when you will have a higher likelihood of encountering a marine mammal," Ezzat said, describing what he characterized as a "probability map." The information analyzed by the computer model includes all the underwater glider and satellite-based data collected by scientists at the Rutgers University Center for Ocean Observing Leadership dating back to 1992, when it was established by then assistant professor Scott Glenn, now a distinguished professor in the Department of Marine and Coastal Sciences. The analysis also included satellite data products made publicly available by the University of Delaware. The underwater gliders are autonomous, torpedo-shaped vessels that zip along under the ocean surface of the mid-Atlantic coast. They are designed to measure many different aspects of seawater, including temperature, salinity, current strength and chlorophyll levels. The gliders also bounce sound waves off schools of fish to gauge their size and record the underwater calls of whales and other marine mammals, locating them in time and space. Satellite data includes measurements of sea surface temperature, water color, and fronts, among others. "We've had the data but, until now, we've not been able to put the two sets - those detections of where the whales are, and what the environment is like at those places - together," Kohut said. "This is a demonstration of the power of employing AI methodologies to advance our ability to predict or estimate where these whales are." Other Rutgers scientists on the study included: Laura Nazzaro, a lab manager in the Department of Marine and Coastal Sciences; and Jeeva Ramasamy, an undergraduate majoring in computer science.

Scientists harness AI to help protect whales, advancing ocean conservation and planning

Using an AI-powered computer program that learns from patterns detected between two vast databases, the researchers said their method improved upon present abilities to monitor the ocean for the distribution of important marine species, such as the critically endangered North Atlantic right whale. North Atlantic right whales have been listed as endangered under the Endangered Species Act since 1970. There are approximately 370 individuals remaining, including about 70 reproductively active females, according to the U.S. National Oceanic and Atmospheric Administration. The researchers' report was published in Nature Scientific Reports. The effort was led by Ahmed Aziz Ezzat, an assistant professor in the Department of Industrial and Systems Engineering at the School of Engineering, and Josh Kohut, a professor in marine sciences who in January became dean of research at the School of Environmental and Biological Sciences. Ezzat leads a research group on applied machine learning for engineering and physical sciences. Jiaxiang Ji, the paper's first author and a doctoral student in the School of Engineering, contributed significantly to the project. Kohut likened the output of the program to what might be learned by tracking the movements of people in a house as well as determining whether there is food in the kitchen and a television set on in the den. Such factors might determine why people are where they are at certain times of the day. Detecting certain patterns, he said, conveys predictive power. "With this program, we're correlating the position of a whale in the ocean with environmental conditions," Kohut said. "This allows us to become much more informed on decision making about where the whales might be. We can predict the time and location that represents a higher probability for whales to be around. This will enable us to implement different mitigation strategies to protect them." Initially, the researchers sought to develop high-resolution models of the North Atlantic right whale presence to support responsible offshore wind farm development and operation. But they said the results have far broader implications and have made the details public as an addendum to their research paper. "These tools are valuable and would solidly benefit anyone engaged in the blue economy -- including fishing, shipping and developing alternative forms of energy sustainably," Ezzat said. "This approach can support a wise and environmentally responsible use of these waters so that we achieve our economic objectives, and at the same time make sure that we cause minimal to no harm to the environmental habitat of these creatures." Unlike typical computer programs, where instructions are explicitly written out, the machine-learning program employed by the researchers analyzed large data sets to discover patterns and relationships. As the AI program encountered more data, it adjusted its internal model to make better predictions or classifications. "The outcome of the machine-learning model is basically a prediction of where and when you will have a higher likelihood of encountering a marine mammal," Ezzat said, describing what he characterized as a "probability map." The information analyzed by the computer model includes all the underwater glider and satellite-based data collected by scientists at the Rutgers University Center for Ocean Observing Leadership dating back to 1992, when it was established by then assistant professor Scott Glenn, now a distinguished professor in the Department of Marine and Coastal Sciences. The analysis also included satellite data products made publicly available by the University of Delaware. The underwater gliders are autonomous, torpedo-shaped vessels that zip along under the ocean surface of the mid-Atlantic coast. They are designed to measure many different aspects of seawater, including temperature, salinity, current strength and chlorophyll levels. The gliders also bounce sound waves off schools of fish to gauge their size and record the underwater calls of whales and other marine mammals, locating them in time and space. Satellite data includes measurements of sea surface temperature, water color, and fronts, among others. "We've had the data but, until now, we've not been able to put the two sets -- those detections of where the whales are, and what the environment is like at those places -- together," Kohut said. "This is a demonstration of the power of employing AI methodologies to advance our ability to predict or estimate where these whales are." Other Rutgers scientists on the study included: Laura Nazzaro, a lab manager in the Department of Marine and Coastal Sciences; and Jeeva Ramasamy, an undergraduate majoring in computer science.