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2024 Journal Impact Factor - 0.6
2024 CiteScore - 1.9
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
Big Data Analytics for Weather Prediction Integrating Regression and ARIMA Models to Assess the Impact of Climate Variability on Fishermen Safety and Maritime Operations
1 Surabaya Maritime Polytechnic, Surabaya, East Java, Indonesia
2 INTI International University, Nilai, Malaysia
3 Udayana University, Denpasar, Bali, Indonesia
2 INTI International University, Nilai, Malaysia
3 Udayana University, Denpasar, Bali, Indonesia
ABSTRACT: Objectives: This study aims to develop an AI-based early warning system for maritime navigation by integrating machine learning techniques to predict weather conditions and assess navigation risks. The research focuses on improving forecasting accuracy for key meteorological and oceanographic variables to enhance navigational safety. Theoretical Framework: The study is grounded in predictive analytics and artificial intelligence applications in maritime risk assessment. It leverages machine learning models, including ARIMA, Random Forest, SVM, and Artificial Neural Networks, to enhance the accuracy of weather and sea condition forecasts, providing valuable insights for maritime operations. Method: The research employs a data-driven approach, utilizing historical meteorological and oceanographic data to train and evaluate machine learning models. Variables such as air temperature, wind speed, sea temperature, rainfall, and air pressure are analyzed using regression, time-series analysis, and statistical modeling techniques to develop an effective predictive system. Results and Discussion: The findings reveal that AI models, particularly ARIMA and regression analysis, demonstrate high predictive capability for air temperature variations. However, dataset limitations and model parameter tuning impact accuracy. The results highlight the importance of selecting appropriate variables and optimizing model structures to improve forecasting reliability. Research Implications: The study contributes to maritime safety by providing a framework for real-time weather forecasting and risk assessment. The findings can inform decision-making in vessel operations and policy development for maritime safety regulations. Originality/Value: This research integrates AI and predictive analytics to enhance maritime navigation safety, addressing gaps in real-time risk assessment and forecasting. The proposed framework provides a foundation for further advancements in AI-driven maritime decision support systems.
KEYWORDS:
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Citation note:
Suwondo I., Setiawan A., Sutoyo S., Wou Onn C., Dewi D.A., Ika Marini N.M.: Big Data Analytics for Weather Prediction Integrating Regression and ARIMA Models to Assess the Impact of Climate Variability on Fishermen Safety and Maritime Operations. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 19, No. 4, doi:10.12716/1001.19.04.09, pp. 1121-1129, 2025
Authors in other databases:
Iie Suwondo:
A. Setiawan:
S. Sutoyo:
C. Wou Onn:
55012068200
16XPxHUAAAAJN.M. Ika Marini:
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