AI-Driven Estimation of Vessel Sailing Times and Underwater Acoustic Pressure for Optimizing Maritime Logistics

  1. Martínez, Rosa 1
  2. García, Jose Antonio 1
  3. Felis, Ivan 1
  1. 1 Centro Tecnológico Naval y del Mar
Actas:
10th International Electronic Conference on Sensors and Applications (ECSA-10)

Año de publicación: 2023

Tipo: Aportación congreso

DOI: 10.3390/ECSA-10-16091 GOOGLE SCHOLAR lock_openAcceso abierto editor

Objetivos de desarrollo sostenible

Resumen

This paper presents an innovative AI-based approach to estimate vessel sailing times in port surroundings. Leveraging historical vessel data, including ship characteristics and weather conditions, the model employs preprocessing techniques to enhance accuracy. Additionally, an underwater acoustic propagation model is used to study underwater noise pressure, aligning with environmental goals. The dataset, covering January to December 2022 in the Port of Cartagena, Spain, undergoes analysis, revealing intriguing patterns in ship routes. Employing various ML models, the study selects Random Forest as the most accurate, achieving an R2 of 0.85 and MSE of 0.145. The research showcases promising accuracy, aiding port optimization and environmental impact reduction, advancing maritime logistics with AI.

Referencias bibliográficas

  • (2023, February 22). Review of Maritime Transport 2022|UNCTAD. Available online: https://unctad.org/rmt2022.
  • Nikghadam, (2023), Marit. Econ. Logist., 25, pp. 576, 10.1057/s41278-021-00203-9
  • Vighi, M., Boschetti, S.T., and Hanke, G. (2021). Marine Strategy Framework Directive Review and Analysis of EU Member States’ 2018 Reports, Publications Office of the European Union.
  • McKenna, (2012), J. Acoust. Soc. Am., 131, pp. 92, 10.1121/1.3664100
  • Natekin, (2013), Front. Neurorobot., 7, pp. 21, 10.3389/fnbot.2013.00021
  • El Mrabet, Z., Sugunaraj, N., Ranganathan, P., and Abhyankar, S. (2022). Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems. Sensors, 22.