Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations

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dc.contributor.authorXu, Lily
dc.contributor.authorGholami, Shahrzad
dc.contributor.authorMc Carthy, Sara
dc.contributor.authorDilkina, Bistra
dc.contributor.authorPlumptre, Andrew
dc.contributor.authorTambe, Milind
dc.contributor.authorSingh, Rohit
dc.contributor.authorNsubuga, Mustapha
dc.contributor.authorMabonga, Joshua
dc.contributor.authorDriciru, Margaret
dc.contributor.authorWanyama, Fred
dc.contributor.authorRwetsiba, Aggrey
dc.contributor.authorOkello, Tom
dc.contributor.authorEnyel, Eric
dc.date.accessioned2022-12-26T19:51:57Z
dc.date.available2022-12-26T19:51:57Z
dc.date.issued2019
dc.description.abstractIllegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software.en_US
dc.identifier.citationXu, L., Gholami, S., McCarthy, S., Dilkina, B., Plumptre, A., Tambe, M., ... & Enyel, E. (2020, April). Stay ahead of Poachers: Illegal wildlife poaching prediction and patrol planning under uncertainty with field test evaluations (Short Version). In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1898-1901). IEEE.en_US
dc.identifier.urihttps://ieeexplore.ieee.org/abstract/document/9101618/
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/6603
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectWildlife protectionen_US
dc.subjectData miningen_US
dc.subjectPredictive modelingen_US
dc.subjectPatrol route planningen_US
dc.subjectPoachingen_US
dc.subjectUncertaintyen_US
dc.titleStay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluationsen_US
dc.typeOtheren_US
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