Combating trade in illegal wood and forest products with machine learning.

Area of Science:

• Environmental science
• Computer science
• International trade

Background:

• Illegal logging and timber trade pose significant threats to ecosystems and communities globally.
• The United States is the world's largest importer of wood and forest products, valued at $78 billion in 2021.
• Detecting illegal timber requires analyzing complex, high-volume shipment data.

Purpose of the Study:

• To develop an automated decision support system for identifying suspicious forest product shipments.
• To leverage machine learning and big data for detecting illegal timber trade.
• To combine domain expertise with automated analysis for enhanced detection capabilities.

Main Methods:

• Formulated the problem as an anomaly detection task using machine learning.
• Collected and integrated forest sector-specific domain knowledge.
• Developed a proof-of-concept framework combining big data analytics and machine learning models.

Main Results:

• Demonstrated the framework's efficacy in flagging anomalous and potentially suspicious shipments.
• Provided an overview of the framework, domain knowledge extraction, and machine learning models.
• Initial analysis confirmed the approach's potential for detecting illicit forest product trade.

Conclusions:

• The developed framework offers an actionable and feasible method for detecting suspicious forest product shipments.
• This approach can assist enforcement agencies in combating illegal logging and associated trade.
• The proof-of-concept lays the foundation for a robust system to safeguard forest resources.