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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: Maritime Risk Assessment, Multivariate Regression Analysis, Big Data, Maritime Weather Prediction, ARIMA Modeling, AI-based Early Warning Systems, Fishermen Safety, Extreme Weather Events
REFERENCES
Priyadharshini, S., Vadivazhagan, K. (2024) Enhanced Vessel Detection In Maritime Surveillance Using Multi-modal Data Integration And Deep Learning 2024 8th International Conference On I-SMAC (Iot In Social, Mobile, Analytics And Cloud) (I-SMAC), 1090-1099 - doi:10.1109/I-SMAC61858.2024.10714663
Jang, H., Yang, W., Kim, H., Lee, D., Kim, Y., Park, J., Jeon, M., Koh, J., Kang, Y., Jung, M., Jung, S., Hao, C., Z., Hin, W., Y., Yihang, C., Kim, A. (2024) MOANA: Multi-Radar Dataset For Maritime Odometry And Autonomous Navigation Application Arxiv Abs/2412.03887 - doi:10.1177/02783649251354897
Kalliovaara, J., Jokela, T., Asadi, M., Majd, A., Hallio, J., Auranen, J., Seppänen, M., Putkonen, A., Koskinen, J., Tuomola, T., Moghaddam, R., M., Paavola, J. (2024) Deep Learning Test Platform For Maritime Applications: Development Of The Em/S Salama Unmanned Surface Vessel And Its Remote Operations Center For Sensor Data Collection And Algorithm Development Remote. Unrated 1545 - doi:10.3390/rs16091545
Otto Bliesner, B. (1999) El Niño/La Niña And Sahel Precipitation During The Middle Holocene Geophysical Research Letters 26 - doi:10.1029/1998GL900236
Guan, C., Hu, S., Mcphaden, M., Wang, F., Gao, S., Hou, Y. (2019) Dipole Structure Of Mixed Layer Salinity In Response To El Niño‐La Niña Asymmetry In The Tropical Pacific Geophysical Research Letters 46, 12165-12172 - doi:10.1029/2019GL084817
Yuniasih, B., Harahap, W., N., Wardana, D., A., S. (2023) El Nino and La Nina Climate Anomalies in Indonesia in 2013-2022 AGROISTA : Journal of Agrotechnology
Hyvärinen, M. (2012) ANALYSIS OF SHIP CASUALTIES IN THE BALTIC, GULF OF FINLAND AND GULF OF BOTNNJA IN 197-972
Eickschen, S. (2000) Wind Speed And SWH Calibration For Radar Altimetry In The North Sea
Petrucci, O., Pasqua, A. (2012) Damaging Events Along Roads During Bad Weather Periods: A Case Study In Calabria (Italy) Natural Hazards And Earth System Sciences 12, 365-378 - doi:10.5194/nhess-12-365-2012
Tolani, H., Neogi, S., Gupta, S., D., Mishra, S., S., Samtani, R. (2024) Analyzing Dynamics Of Extreme Weather Events (EWE) In India: Unfolding Trends Through Statistical Assessment Of 50 Years Data (1970–2019) BMC Environmental Science - doi:10.1186/s44329-024-00012-4
Huang, W., Zheng, S., Du, Z. (2024) Research On Insurance Decision-Making Model For Extreme Weather Based On ARIMA Algorithms 2024 International Conference On Power, Electrical Engineering, Electronics And Control (PEEEC), 1154-1157 - doi:10.1109/PEEEC63877.2024.00213
Noh, S., Lee, S. (2024) Forecasting Meteorological Drought Conditions In South Korea Using A Data-Driven Model With Lagged Global Climate Variability Sustainability - doi:10.3390/su16156485
Brandt, P., Munim, Z., H., Chaal, M., Kang, H. (2024) Maritime Accident Risk Prediction Integrating Weather Data Using Machine Learning Transportation Research Part D: Transport And Environment - doi:10.1016/j.trd.2024.104388
Panda, S., Ray, P. (2023) A Survey On Weather Prediction Using Big Data And Machine Learning Techniques 2023 5th International Conference On Energy, Power And Environment: Towards Flexible Green Energy Technologies (ICEPE), 1-6 - doi:10.1109/ICEPE57949.2023.10201614
Vaishnavi, J., Minmini, V., Panda, M. (2024) Weather And Emission Data Analysis And Prediction Using Machine Learning On A Big Data Platform 2024 15th International Conference On Computing Communication And Networking Technologies (ICCCNT), 1-7 - doi:10.1109/ICCCNT61001.2024.10724518
Mehta, S., Manisha, E. (2023) Preventive And Predictive CNN Based Solution For Pipeline Leak, Blockage And Corrosion Detection International Journal Of Scientific Research In Computer Science, Engineering And Information Technology
Sharma, E., Deo, R., Davey, C., P., Carter, B. (2024) Artificial Intelligence-Empowered Doppler Weather Profile For Low-Earth-Orbit Satellites Sensors (Basel, Switzerland) 24 - doi:10.3390/s24165271
Hasan, M., M. (2024) Regional Analysis Of Extreme Weather Events Using Deep Learning Innovatech Engineering Journal - doi:10.70937/faet.v1i01.38
Li, Y., Goda, K. (2022) Hazard And Risk-Based Tsunami Early Warning Algorithms For Ocean Bottom Sensor S-Net System In Tohoku, Japan, Using Sequential Multiple Linear Regression Geosciences - doi:10.3390/geosciences12090350
Li, Y., Tong, D., Makkaroon, P., Delsole, T., Tang, Y., Campbell, P., Baker, B., Cohen, M., Darmenov, A., Ahmadov, R., James, E., Hyer, E., Xian, P. (2024) Multi-Agency Ensemble Forecast Of Wildfire Air Quality In The United States: Toward Community Consensus Of Early Warning Bulletin Of The American Meteorological Society - doi:10.1175/BAMS-D-23-0208.1
Jaya, I., Handoko, B., Andriyana, Y., Chadidjah, A., Kristiani, F., Antikasari, M. (2023) Multivariate Bayesian Semiparametric Regression Model For Forecasting And Mapping HIV And TB Risks In West Java, Indonesia Mathematics - doi:10.3390/math11173641
Røstad, J., Aarset, M., F. (2024) Boosting Offshore Uptime: Accurate Real-Time Sea State ADIPEC - doi:10.2118/222027-MS
Zahorodnia, Y., Maksymov, S. (2021) COMMERCIAL RISKS IN THE SEA TRANSPORTATION SYSTEM ON THE EXAMPLE OF THE «EVER GIVEN» CONTAINER CARRIER Development Of Management And Entrepreneurship Methods On Transport (ONMU)
Maulida, Z., Hafidzah, N., Purba, D., Kusumawati, E. (2024) Identification of Passage Plan Process with Risk Assessment Analysis Globe: Publication of Engineering, Earth Technology, Marine Science
Kogawa, T., Takayabu, Y. (2013) Environmental Conditions On The Selection Of MJO And Moist Kelvin Waves
Bhatla, R., Bhattacharyya, S., Verma, S., Mall, R., Singh, R., S. (2021) El Nino/La Nina And IOD Impact On Kharif Season Crops Over Western Agro-Climatic Zones Of India Theoretical And Applied Climatology 151, 1355-1368 - doi:10.1007/s00704-023-04361-z
Aji, T., Pranowo, W., S., Asmoro, N., W., Agustinus, A., Kurniawan, M., A., Rahmatullah, A. (2023) The Characteristics Of The Mixed Layer Depth During La Niña, El Niño, And Normal Years In The North Natuna Sea Omni-Aquatics - doi:10.20884/1.oa.2023.19.2.1089
Yuniasih, B., Harahap, W., N., Wardana, D., A., S. (2023) El Nino and La Nina Climate Anomalies in Indonesia in 2013-2022 AGROISTA : Journal of Agrotechnology
(2022) Predictive Maintenance Beyond Prediction Of Failures
Margaretha, R., Syuzairi, M., Mahadiansar, M. (2024) Digital Transformation In The Maritime Industry; Opportunities And Challenges For Indonesia Journal Of Maritime Policy Science - doi:10.31629/jmps.v1i1.7003
Du, Y., Li, C., Wang, T., Xu, Y. (2023) Special Issue On "Smart Port And Shipping Operations" In Maritime Policy & Management Maritime Policy & Management 50, 413-414 - doi:10.1080/03088839.2023.2196754
Nie, W., Chen, J., Song, D., Dong, L., Liu, X., Wang, E. (2024) Three-Dimensional Intelligent Monitoring And Early Warning Technology For Tailings Ponds Based On Spatiotemporal Fusion Of Multisource Big Data. Environmental Monitoring And Assessment 196 11, 1081 - doi:10.1007/s10661-024-13242-5
Salim, S., Hussain, I., Kaur, J., Morita, P. (2023) An Early Warning System For Air Pollution Surveillance: A Big Data Framework To Monitoring Risks Associated With Air Pollution 2023 IEEE International Conference On Big Data (Bigdata), 3371-3374 - doi:10.1109/BigData59044.2023.10386185
Liu, X., Member, J., L., Zhao, Y., Ding, T., Member, X., L., S., Liu, J. (2024) A Bayesian Deep Learning-Based Probabilistic Risk Assessment And Early-Warning Model For Power Systems Considering Meteorological Conditions IEEE Transactions On Industrial Informatics 20, 1516-1527 - doi:10.1109/TII.2023.3278873
Jin, W., Liu, Y., Fang, Y., Wang, P., Liu, L. (2023) Construction And Application Of National Urban Waterlogging Risk Assessment System Based On Big Data 2023 IEEE 14th International Conference On Software Engineering And Service Science (ICSESS), 290-296 - doi:10.1109/ICSESS58500.2023.10293117
Huang, X., Wu, Y., Zhao, X., Zhang, J., Li, J. (2024) Using Big Data Mining Algorithm To Improve The Accuracy Of Risk Assessment Of Key Operating Vehicles 2024 3rd International Conference On Data Analytics, Computing And Artificial Intelligence (ICDACAI), 583-587 - doi:10.1109/ICDACAI65086.2024.00112
Du, Z., Zhu, Y., Li, D. (2024) A Risk Assessment Model For Navigation Safety Of Maritime Aquaculture Platform Based On AIS Ship Trajectory Journal Of Electrical Systems
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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