A combination of machine learning and game theory is being used to fight elephant poaching in Uganda

A combination of machine learning and game theory is being used to fight elephant poaching in Uganda

A combination of machine learning and game theory is being used to fight elephant poaching in Uganda

Africa’s wildlife is in a constant state of danger.

Between 2009 and 2015, Tanzania and Mozambique lost more than half of their elephants, many of them to poaching for ivory smuggling. The decline has propelled African vulture populations, who feed on elephant carcasses, toward extinction too. And attempts at curtailing poaching and ivory smuggling haven’t helped the dwindling elephant population. In South Africa, rhinos are a prized poaching target too, for their horns. The attempts to keep poachers at bay having failed, some conservationists have proposed the expensive alternative of airlifting rhinos away from poaching sites.

Uganda, which remains “heavily implicated” in the illegal ivory trade according to the monitoring body CITES, is now testing a more direct way to crack down on the illegal hunters before they even get to the animals. Using Protection Assistant for Wildlife Security (PAWS), a technology combining machine learning and game theory, researchers can predict where poachers may attack and tell rangers where to patrol.

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“The basic idea is that you have limited resources, you can’t be everywhere all the time,” University of Southern California professor Milind Tambe, who’s leading the initiative, told Quartz. “Where and when should you do patrol?”

To make their predictions, researchers studied 12 years worth of data collected by rangers, from 2003 to 2015, provided by the Wildlife Conservation Society. These included reports of past attacks, snare placements, and other illegal activities.

 



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