AIを用いた象の密猟防止（How AI can help prevent elephant poaching）

2025-02-25

2025-02-24 カーディフ大学

カーディフ大学の研究チームは、人工知能（AI）を活用して象の密猟を防止する新たな手法を開発しました。この手法では、AIを用いて密猟者の行動パターンを分析し、密猟が行われる可能性の高い地域や時間帯を特定します。これにより、野生生物保護団体や当局は、リソースを最適に配置し、効果的なパトロールや監視活動を行うことが可能となります。このアプローチは、象の保護だけでなく、他の野生生物の密猟防止にも応用できると期待されています。

＜関連情報＞

PoachNet：オントロジーベースの知識グラフによる密猟の予測 PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph

Naeima Hamed,Omer Rana,Pablo Orozco-terWengel,Benoît Goossens and Charith Perera
Sensors Published: 20 December 2024
DOI:https://doi.org/10.3390/s24248142

Abstract

Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. Using elephant GPS data extracted from an ontology-based knowledge graph, PoachNet employs a sequential neural network to predict future movements, which are semantically modelled and incorporated into the graph. Semantic Web Rule Language (SWRL) is applied to infer poaching risk based on these geo-location predictions and poaching rule-based logic. By addressing spatiotemporal complexity and integrating predictions into an actionable semantic rule, PoachNet advances the field, with its geo-location prediction model outperforming state-of-the-art approaches.