Research on an Improved SVM-RF-Based Risk Assessment Algorithm for Infectious Substances at Port of Entry

Jin Li, Chen Li (), Yong Bian, Fengze Wu and Jie Tian
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Jin Li: College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Chen Li: College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Yong Bian: China Customs Science and Technology Research Center, Beijing 100026, China
Fengze Wu: College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Jie Tian: China Customs Science and Technology Research Center, Beijing 100026, China

Sustainability, 2025, vol. 17, issue 21, 1-18

Abstract: The wide variety of infectious substances encountered at ports of entry, coupled with complex risk profiles and the challenges of subjective identification, make it difficult for assessors to conduct rapid, accurate, and objective evaluations, particularly given limitations in expertise and experience. To address this challenge and to develop a highly generalizable risk assessment model for infectious substances, this study draws on a five-year case database of risk incidents at Beijing Customs ports. Frequency analysis was first employed to identify key risk factors associated with infectious substances entering ports. Subsequently, risk assessment models were constructed using decision tree, random forest, and support vector machine algorithms, as well as an improved SVM-RF algorithm, followed by an analysis of feature importance. The results demonstrate that the improved SVM-RF algorithm achieved superior generalization performance, with an evaluation accuracy of 0.93. To further validate its applicability and feasibility, the improved model was applied to real cases of infectious substances intercepted at Beijing Customs ports, where its risk level classifications were consistent with expert assessments. These findings provide a valuable reference for improving the customs safety assessment system for special biological resources and for mitigating the risks posed by infectious substances at ports of entry.

Keywords: port infectious substance risk; risk level assessment; SVM-RF; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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