New era in wildlife conservation: How AI will help protect elephants

A cutting-edge artificial intelligence system promises to help protect elephants in Malaysia from illegal hunting. 

By blending predictive analytics with insights into elephant behavior, the technology aims to shield these animals from escalating threats that have long jeopardized their survival.

Developed through a collaborative effort at Cardiff University, PoachNet combines deep learning, GPS-based data, and ecological insights to gauge and mitigate poaching hazards. 

The tool has been designed specifically to forecast where elephants are most likely to be found and then assess each location’s risk level, offering a proactive approach that goes beyond routine surveillance.

Scientists from the School of Computer Science and Informatics and the School of Biosciences worked together to construct a platform capable of learning from historical elephant movement patterns and detecting threats in near-real time. 

By applying advanced machine-learning techniques, the research team can predict where elephants are headed, identify hazardous zones, and assist on-the-ground teams in allocating resources effectively.

A better way to protect elephants 

Central to PoachNet’s operation is a knowledge graph that compiles diverse information about elephant movements and known risk factors. This structured repository is linked to a specialized machine-learning model that interprets data from elephant GPS trackers. 

“Elephant GPS data is analyzed with a special type of AI – a sequential neural network – to predict their movements,” explained study lead author Naeima Hamed, a PhD student at Cardiff.

“These predictions are added to the knowledge graph in a meaningful way – then PoachNet uses a rule-based system to apply poaching rules and detect hidden patterns in the data.”

The platform’s predictive ability sets it apart from more conventional methods of wildlife protection. 

“We found that, when tested, PoachNet was more accurate than other leading methods, consistently performing better. By handling the complexity of time and space data and turning predictions into practical rules, PoachNet offers a big improvement in tracking and protecting elephants,” said Hamed.

Predicting poaching before it happens

Before PoachNet, strategies to curb poaching often relied on single approaches – such as analyzing ranger patrol logs or examining evidence captured by camera traps. 

However, these methods do not always account for the dynamic ways elephants move or the full spectrum of human impacts on their habitats.

PoachNet integrates broader knowledge of wildlife behavior, enabling the system to recognize emergent patterns that may indicate an imminent poaching threat.

The project’s designers hope that by identifying where elephants are most at risk, authorities in Sabah can mobilize anti-poaching patrols and camera traps in the most vulnerable areas. 

This targeted response could lead to more efficient use of conservation resources, focusing on regions where illegal activity is statistically more likely to occur.

Broader implications for conservation

Study co-author Omer Rana from Cardiff’s School of Computer Science and Informatics highlighted the system’s potential to extend beyond elephant conservation in Malaysia. 

“PoachNet is a unique software tool that integrates semantically modeled regional data sources with emerging machine learning algorithms and semantic reasoning. PoachNet addresses a challenge that continues to affect communities supporting endangered species,” said Rana.

“Both climate change and economics are leading to significant impact across this interface between human activity and natural habitats. The data-driven approach adopted in PoachNet can also be generalized to support other similar localities and national parks – enabling more efficient use of law enforcement and government resources.”

While PoachNet is currently attuned to Bornean elephants, the underlying architecture could be adapted to protect various endangered species worldwide, as each instance might require only modest adjustments in the knowledge graph and relevant predictive models.

Urgent need to protect elephants

Efforts to shield Bornean elephants from poaching have never been more critical. As habitat areas shrink and the illegal ivory trade persists, these animals face a grim reality. 

According to co-author Benoit Goossens, a scientist at Cardiff University’s School of Biosciences and director of the Danau Girang Field Center, “habitat loss, human-elephant conflict, and poaching threaten Bornean elephants. 

“Despite global anti-poaching efforts, the illegal ivory trade continues to drive poaching, reducing the population to fewer than 1500. We hope that PoachNet can assist in poaching prevention methods, therefore helping to ensure the safety of the elephant population in Sabah for the future.”

Given that traditional mechanisms to combat poaching must constantly evolve, there is an urgent need for innovative solutions like PoachNet that incorporate modern technologies and up-to-date ecological knowledge. 

By matching predictive models with ground-level patrolling and international regulatory measures, the system could help reverse some of the most destructive trends endangering these elephants.

The future of wildlife protection 

In the long term, the research team plans to enhance PoachNet by introducing additional data sources.

This could involve tapping into acoustic sensors that detect gunshots or vehicle noises, as well as using satellite imagery to keep track of land-use changes and growing human activities. 

Such expansions would feed the system with even more contextual information, improving its predictive power and enabling faster, more decisive responses.

The ultimate objective is to give conservation authorities an advanced tool capable of anticipating illegal activities, thereby reducing the risk to Bornean elephants and potentially other species in peril. 

By blending scientific rigor, state-of-the-art AI, and on-the-ground intelligence, PoachNet represents a considerable step forward in wildlife protection strategies.

As poaching incidents continue to imperil elephant populations, the hope is that this novel system will allow for data-driven vigilance, pinpointing threats before they escalate into irreversible harm. 

With more accurate forecasts and the flexibility to adapt to new conditions, PoachNet may well serve as a blueprint for conservation efforts aimed at preserving some of Earth’s most iconic and vulnerable animals.

Details of this system appear in the research paper, “PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph,” published in the journal Sensors. 

—–

Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.

Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

—–