The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Proactive Environmental Crime Detection Using Machine Learning and Multi-Source Data Fusion
|
|
Author(s): Yuqian Liu (Zhengzhou Railway Vocational & Technical College, China), Kairui Li (Zhengzhou Railway Vocational & Technical College, China)and Mi Li (Guangxi Police College, China)
Copyright: 2026
Volume: 18
Issue: 1
Pages: 13
Source title:
International Journal of Digital Crime and Forensics (IJDCF)
Editor(s)-in-Chief: Feng Liu (Chinese Academy of Sciences, China)
DOI: 10.4018/IJDCF.408837
Purchase
|
Abstract
Traditional methods struggle to detect concealed environmental crimes. This study proposes an intelligent framework that uses machine learning and multi-source data fusion (environmental, operational, and logistics) to proactively identify risks such as illegal discharge and waste transfer. Empirical results show improved timeliness and accuracy of detection. Despite high initial costs, long-term economic benefits are substantial. Challenges remain in algorithmic transparency, data sharing, and legal compliance. The framework provides actionable intelligence for law enforcement, bridges data silos to enable coordinated responses, and contributes to digital forensics and sustainable governance. Future integration with blockchain technology could enhance the integrity of digital evidence for prosecution.
Related Content
|
Xixiang Yin.
© 2026.
15 pages.
|
|
Lihua Wang, Pengfei Pei, Yiran He, Zihuan Huang, Shuai Hu.
© 2026.
23 pages.
|
|
Shivalaxmi Arumugham, P. Ranjit Jeba Thangaiah.
© 2026.
20 pages.
|
|
Yuqian Liu, Kairui Li, Mi Li.
© 2026.
13 pages.
|
|
Waleed A. Alrodhan.
© 2026.
33 pages.
|
|
Ling An.
© 2025.
19 pages.
|
|
Mi Li.
© 2025.
18 pages.
|
|
|