Prediction of ship incidents based on BP neural network

Haoyuan Zheng; Hongbin Xie; Chen Wu

doi:10.1117/12.3034908

7 August 2024 Prediction of ship incidents based on BP neural network

Haoyuan Zheng, Hongbin Xie, Chen Wu

Author Affiliations +

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 1322404 (2024) https://doi.org/10.1117/12.3034908
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

This paper investigates the use of a Back-Propagation Neural Network (BPNN) to identify the correlation between relevant parameters and ship collisions. Subsequently, a model is constructed to forecast the number of collisions over the upcoming three years. This model is expected to provide insight into the prevalence of maritime incidents and aid in the formulation of preventative measures. In this paper, BP neural network is used to study the water incidents provided by TSB (The Transportation Safety Board of Canada). The purpose of this paper is to predict the number of collisions in the future from the macro level. To achieve this goal, this paper establishes a data model based on Python. The results of this study can help researchers and channel management agencies better understand collision risk.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haoyuan Zheng, Hongbin Xie, and Chen Wu "Prediction of ship incidents based on BP neural network", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 1322404 (7 August 2024); https://doi.org/10.1117/12.3034908

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