AccScience Publishing / IJOSI / Volume 8 / Issue 4 / DOI: 10.6977/IJoSI.202412_8(4).0009
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ARTICLE

Exploring maritime movement information: an explainable AIap-proach using Hi-DBSCANand SHAPanalysis

Nitin Newaliya1 ,  Vikas Siwach1* ,  Harkesh Sehrawat1 ,  Yudhvir Singh1
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1 Department of Computer Science & Engineering, UIET, Maharshi Dayanand University, Rohtak, Haryana, India
IJOSI 2024 , 8(4), 131–145; https://doi.org/10.6977/IJoSI.202412_8(4).0009
Submitted: 25 June 2024 | Revised: 1 September 2024 | Accepted: 18 October 2024 | Published: 30 December 2024
© 2024 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Maritime movement information is pivotal for several applications, including monitoring and examining vessel activities, ensuring efficient and secure navigation, logistics optimisation, and enhancing safety and environmental protection. The maritime industry relies on Automatic Identification System (AIS) data, which provides infor-mation on movement of vessels at sea. Determining meaningful and useful insights from this data is a challenge. The complexity and volume of the information make it difficult for traditional methods to provide in-depth insights and explanations. This paper presents an innovative Explainable AI (XAI) approach to explore maritime movement information using AIS data by leveraging high dimensional Density-Based Spatial Clustering of Applications with Noise (Hi-DBSCAN) algorithm and SHAP (SHapley Additive exPlanations) values in a novel way. Through ex-periments using real AIS datasets, the study reveals the efficacy of SHAP in determining the influence of AIS features on cluster formation. Results from two distinct AIS datasets demonstrate the efficacy of this method. This approach effectively unravels the ‘black box’ nature of clustering, providing maritime stakeholders with a clearer understanding of vessel behaviour patterns. For instance, in one dataset, the course of the vessel was identified as the most significant feature impacting clustering outcomes. Furthermore, the study explores SHAP's potential for anomaly detection by identifying data points with inconsistent feature influences. This study demonstrates that integrating Hi-DBSCAN clustering with SHAP analysis offers a transparent and interpretable method for under-standing vessel behaviour patterns from maritime movement information and extraction of meaningful insights. This framework provides maritime stakeholders with insights beyond traditional pattern recognition, with trans-parency and explainability, allowing for a deeper understanding and more informed and data-driven decisions in maritime operations.

Keywords
Data Analytics
DBSCAN
Explainable AI
AIS
SHAP.
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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