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Prediction of Container Damage Insurance Claims for Optimized Maritime Port Operations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10832)


A company operating in a commercial maritime port often experiences clients filing insurance claims on damaged shipping containers. In this work, multiple classifiers have been trained on synthesized data, to predict such insurance claims. The results show that Random Forests outperform other classifiers on typical machine learning metrics. Further, insights into the importance of various features in this prediction are discussed, and their deviation from expert opinions. This information facilitates selective information collation to predict container claims, and to rank data sources by relevance. To our knowledge, this is the first publication to investigate the factors associated with container damage and claims, as opposed to ship damage or other related problems.


  • Damage Containment
  • Data Source Ranking
  • Container Ships
  • Machine Learning Metrics
  • Relevant Association Rules

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 1.
Fig. 2.


  1. 1.

    The survey (named Bottlenecks in Port Operations) was distributed by a Google Forms link in July 2017, after receiving the necessary approval from the Research Ethics Board of the University of Ottawa.

  2. 2.

    The survey results showed that cargo value, hazardous and/or sensitive cargo were the most important attributes in predicting insurance claims.


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We would like to thank SciNet on whose infrastructure our experiments were run (Each GPC node runs Intel’s Xeon E5540 8-core CPU at 2.53 GHz, with 16 GB RAM; each P7 node runs an IBM Power 755 server with four 8-core 3.3 GHz Power7 CPUs and 128 GB RAM. Detailed specifications can be found at We would also like to thank Montreal Gateway Terminals Partnership and their on-staff domain experts, and professional highway truck driver Alyssa Fred Wai-Yi Wong, for sharing their expertise used in data creation and validation.

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Correspondence to Ashwin Panchapakesan .

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Panchapakesan, A., Abielmona, R., Falcon, R., Petriu, E. (2018). Prediction of Container Damage Insurance Claims for Optimized Maritime Port Operations. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham.

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