A Novel Method for Assessing Pirate Attack Risks and Spatial Distribution
Keywords:
pirate attacks, risk analysis, K-means clustering, Autoencoder, EWM-TOPSISAbstract
Pirate attacks pose one of the most severe challenges to the safety of maritime navigation. Effectively quantifying the risk of pirate attacks and understanding their spatial distribution through historical records is crucial for planning safe shipping routes. Given the diverse data types and multiple factors involved in assessing pirate attacks recorded in the Global Integrated Shipping Information System (GISIS) database, we propose a spatiotemporal influence factor analysis model based on the K-means clustering algorithm. Features are encoded using an Autoencoder, and the evaluation is conducted using the Entropy Weight Method- Technique for Order Preference by Similarity to Ideal Solution (EWM-TOPSIS). The model then simulates and predicts the geographical distribution of pirate risks. The results indicate that the model effectively captures the geographical distribution patterns of pirate attack incidents and successfully predicts the risk distribution across different sea areas. This approach aids in ship route planning and reduces the risk of pirate attacks.
References
A. S. Grzelakowski, J. Herdzik, S. Skiba (2022) Maritime shipping decarbonization: Roadmap to meet zero-emission target in shipping as a link in the global supply chains. 15, 6150. https://doi.org/10.3390/en15176150
M. M. O. Ehizuelen (2023) Assessing the national and regional effectiveness of countering maritime piracy in the Gulf of Guinea. 88, 3549-3574. https://doi.org/10.1007/s10708-022-10823-0
M. C. Robitaille (2020) Maritime piracy and international trade. 31, 957-974. https://doi.org/10.1080/10242694.2019.1627511
C. J. Nwalozie (2020) Exploring contemporary sea piracy in Nigeria, the Niger Delta and the Gulf of Guinea. 13, 159-178. https://doi.org/10.1007/s12198-020-00218-y
G. L. Denton, J. R. Harris (2022) Maritime piracy, military capacity, and institutions in the Gulf of Guinea. 34, 1-27. https://doi.org/10.1080/09546553.2019.1659783
J. Regan (2022) Varied Incident Rates of Global. 32, 374-387. https://doi.org/10.1177/1057567720944448
M. Jiang, J. Lu (2020) The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network. 139, 101965. https://doi.org/10.1016/j.tre.2020.101965
H. Fan, J. Lu, Z. Chang, Y. Ji (2023) A Bayesian network-based TOPSIS framework to dynamically control the risk of maritime piracy. 1-20. https://doi.org/10.1080/03088839.2023.2193585
J. J. Dabrowski, J. P. De Villiers (2015) Maritime piracy situation modelling with dynamic Bayesian networks. 23, 116-130. https://doi.org/10.1016/j.inffus.2014.07.001
X. Gong, H. Jiang, D. Yang (2023) Maritime piracy risk assessment and policy implications: A two-step approach. 150, 105547. https://doi.org/10.1016/j.marpol.2023.105547
O. Vaněk, M. Jakob, O. Hrstka, M. Pěchouček (2013) Agent-based model of maritime traffic in piracy-affected waters. 36, 157-176. https://doi.org/10.1016/j.trc.2013.08.009
M. Jin, W. Shi, K. C. Lin, K. X. Li (2019) Marine piracy prediction and prevention: Policy implications. 108, 103528. https://doi.org/10.1016/j.marpol.2019.103528
S. Pristrom, Z. Yang, J. Wang, X. Yan (2016) A novel flexible model for piracy and robbery assessment of merchant ship operations. 155, 196-211. https://doi.org/10.1016/j.ress.2016.07.001
U. Daxecker, B. C. Prins (2017) Financing rebellion: Using piracy to explain and predict conflict intensity in Africa and Southeast Asia. 54, 215-230. https://doi.org/10.1177/0022343316683436
P. Govender, V. Sivakumar (2020) Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). 11, 40-56. https://doi.org/10.1016/j.apr.2019.09.009
Y. Wang, H. Yao, S. Zhao, Y.E.F. Zheng (2015) Dimensionality reduction strategy based on auto-encoder. https://doi.org/10.1145/2808492.2808555
U. Demšar, P. Harris, C. Brunsdon, A. S. Fotheringham, S. McLoone (2013) Principal component analysis on spatial data: an overview. 103, 106-128. https://doi.org/10.1080/00045608.2012.689236
A. G. Yong, S. Pearce (2013) A beginner's guide to factor analysis: Focusing on exploratory factor analysis. 9, 79-94. https://doi.org/10.20982/tqmp.09.2.p079
Z. Li, Z. Luo, Y. Wang, G. Fan, J. Zhang (2022) Suitability evaluation system for the shallow geothermal energy implementation in region by Entropy Weight Method and TOPSIS method. 184, 564-576. https://doi.org/10.1016/j.renene.2021.11.112
A. E. Torkayesh, M. Deveci, S. Karagoz, J. Antucheviciene (2023) A state-of-the-art survey of evaluation based on distance from average solution (EDAS): Developments and applications. 221, 119724. https://doi.org/10.1016/j.eswa.2023.119724
K. S. Ni, T. Q. Nguyen (2009) An adaptable k-nearest neighbors algorithm for MMSE image interpolation. 18, 1976-1987. https://doi.org/10.1109/TIP.2009.2023706
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