Artificial Intelligence in Integrated Marine Observing Systems: A Comprehensive Review

Soni Adiyono; Muhammad Arifin; Noor Latifah; Eko Darmanto

doi:10.12962/j25481479.v10i1.22308

Artificial Intelligence in Integrated Marine Observing Systems: A Comprehensive Review

Soni Adiyono, Muhammad Arifin, Noor Latifah, Eko Darmanto

Abstract

The marine ecosystem is vital for sustaining life on Earth, yet its vastness and complexity present significant challenges for effective monitoring and management. Integrated Marine Observing Systems (IMOS) have emerged as essential tools for understanding and protecting marine environments. This study aims to systematically review the integration of artificial intelligence (AI) into IMOS, focusing on its contributions to data processing, biodiversity monitoring, and environmental change analysis. A systematic literature review (SLR) method is employed to analyze existing research and identify key AI techniques and their applications in marine and oceanographic studies. Results indicate that deep learning is the most widely used AI method, with marine research being the primary application domain. Other areas, such as environmental monitoring and industrial systems, also demonstrate considerable potential. However, data inconsistency, operational limitations, and the lack of standardized frameworks remain significant barriers. This review highlights the transformative role of AI in enhancing IMOS capabilities and provides recommendations for addressing existing challenges to support sustainable marine management.

Keywords

Artificial Intelligence; Marine; Integrated Marine Observing Systems (IMOS); Systematic Literature Review

Full Text:

PDF

References

S. Serrao-Neumann and A. Coudrain, “Science and Knowledge Production for Climate Change Adaptation: Challenges and Opportunities,” in Springer Climate, Faculty of Arts and Social Sciences, The University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand: Springer, 2018, pp. 3–11. doi: 10.1007/978-3-319-74669-2_1.

Y. Liu, M. Qiu, C. Liu, and Z. Guo, “Big Data in Ocean Observation: Opportunities and Challenges BT - Big Data Computing and Communications,” Y. Wang, G. Yu, Y. Zhang, Z. Han, and G. Wang, Eds., Cham: Springer International Publishing, 2016, pp. 212–222.

M. Lin and C. Yang, “Ocean Observation Technologies: A Review,” Chinese J. Mech. Eng., vol. 33, no. 1, p. 32, 2020, doi: 10.1186/s10033-020-00449-z.

T. Zhao et al., “Artificial intelligence for geoscience: Progress, challenges, and perspectives,” Innov., vol. 5, no. 5, p. 100691, 2024, doi: https://doi.org/10.1016/j.xinn.2024.100691.

M. Amani et al., “Ocean Remote Sensing Techniques and Applications: A Review (Part II),” Water, vol. 14, no. 21. 2022. doi: 10.3390/w14213401.

D. Neupane and J. Seok, “A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition,” Electronics, vol. 9, no. 11. 2020. doi: 10.3390/electronics9111972.

M. Francisco and B.-O. Linnér, “AI and the governance of sustainable development. An idea analysis of the European Union, the United Nations, and the World Economic Forum,” Environ. Sci. Policy, vol. 150, p. 103590, 2023, doi: https://doi.org/10.1016/j.envsci.2023.103590.

S. Adiyono and S. Novianto, “Game Method Based on Genre Game as Higher Educational Learning : Systematic Literature Review with VOSviewer,” pp. 23–33, 2023.

S. N. Ismail, A. Ramli, and H. A. Aziz, “Influencing factors on safety culture in mining industry: A systematic literature review approach,” Resour. Policy, vol. 74, p. 102250, 2021, doi: https://doi.org/10.1016/j.resourpol.2021.102250.

F. Jingga, R. Kosala, B. Ranti, and S. H. Supangkat, “It governance implementation in indonesia: A systematic literature review,” Int. J. Sci. Technol. Res., vol. 8, no. 10, pp. 2074–2079, 2019.

I. J. B. Young, S. Luz, and N. Lone, “A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis.,” Int. J. Med. Inform., vol. 132, p. 103971, Dec. 2019, doi: 10.1016/j.ijmedinf.2019.103971.

T. Saheb and L. Izadi, “Paradigm of IoT big data analytics in the healthcare industry: A review of scientific literature and mapping of research trends,” Telemat. Informatics, vol. 41, pp. 70–85, 2019, doi: https://doi.org/10.1016/j.tele.2019.03.005.

K. P. Gupta, P. Bhaskar, and S. Singh, “Prioritization of factors influencing employee adoption of e-government using the analytic hierarchy process,” J. Syst. Inf. Technol., vol. 19, no. 1/2, pp. 116–137, Jan. 2017, doi: 10.1108/JSIT-04-2017-0028.

S. Jones, “eGovernment Document Management System: A case analysis of risk and reward,” Int. J. Inf. Manage., vol. 32, no. 4, pp. 396–400, 2012, doi: https://doi.org/10.1016/j.ijinfomgt.2012.04.002.

J. Jenrette, Z. Y.-C. Liu, P. Chimote, T. Hastie, E. Fox, and F. Ferretti, “Shark detection and classification with machine learning,” Ecol. Inform., vol. 69, 2022, doi: 10.1016/j.ecoinf.2022.101673.

J. Zhang, X. Zhang, X. Wang, P. Ning, and A. Zhang, “Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model,” Ocean Model., vol. 184, 2023, doi: 10.1016/j.ocemod.2023.102232.

F. C. Minuzzi and L. Farina, “A deep learning approach to predict significant wave height using long short-term memory,” Ocean Model., vol. 181, 2023, doi: 10.1016/j.ocemod.2022.102151.

K. Liao, Y. Song, X. Nie, L. Liu, and S. Qi, “Suspended Sediment Concentrate Estimation From Landsat Imagery and Hydrological Station in Poyang Lake Using Machine Learning,” IEEE Access, vol. 12, pp. 85411–85422, 2024, doi: 10.1109/ACCESS.2024.3414996.

R. Gorkin et al., “Sharkeye: Real-time autonomous personal shark alerting via aerial surveillance,” Drones, vol. 4, no. 2, pp. 1–17, 2020, doi: 10.3390/drones4020018.

Z. Lv, D. Chen, H. Feng, W. Wei, and H. Lv, “Artificial Intelligence in Underwater Digital Twins Sensor Networks,” ACM Trans. Sens. Networks, vol. 18, no. 3, 2022, doi: 10.1145/3519301.

D. Wu et al., “The PlastOPol system for marine litter monitoring by citizen scientists,” Environ. Model. Softw., vol. 169, 2023, doi: 10.1016/j.envsoft.2023.105784.

A. S. Ferreira et al., “Advancing multi-vehicle deployments in oceanographic field experiments,” Auton. Robots, vol. 43, no. 6, pp. 1555–1574, 2019, doi: 10.1007/s10514-018-9810-x.

P. Prasad, V. J. Loveson, and M. Kotha, “Probabilistic coastal wetland mapping with integration of optical, SAR and hydro-geomorphic data through stacking ensemble machine learning model,” Ecol. Inform., vol. 77, 2023, doi: 10.1016/j.ecoinf.2023.102273.

E. Kartal and A. Altunkaynak, “Empirical-singular-wavelet based machine learning models for sea level forecasting in the bosphorus strait: A performance analysis,” Ocean Model., vol. 188, 2024, doi: 10.1016/j.ocemod.2024.102324.

Z. Zhang et al., “Combining deep learning with physical parameters in POC and PIC inversion from spaceborne lidar CALIOP,” ISPRS J. Photogramm. Remote Sens., vol. 212, pp. 193–211, 2024, doi: 10.1016/j.isprsjprs.2024.05.007.

C. Post et al., “Possibilities of Real Time Monitoring of Micropollutants in Wastewater using Laser‐Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI),” Sensors, vol. 22, no. 13, 2022, doi: 10.3390/s22134668.

X. Zhao, Y. Li, Y. Chen, X. Qiao, and W. Qian, “Water Chlorophyll a Estimation Using UAV-Based Multispectral Data and Machine Learning,” Drones, vol. 7, no. 1, 2023, doi: 10.3390/drones7010002.

N. Kleanthous, A. J. Hussain, W. Khan, J. Sneddon, A. Al-Shamma’a, and P. Liatsis, “A survey of machine learning approaches in animal behaviour,” Neurocomputing, vol. 491, pp. 442–463, 2022, doi: 10.1016/j.neucom.2021.10.126.

G. Coro, P. Bove, I. Baneschi, A. Bertini, L. Calvisi, and A. Provenzale, “Climate change effects on animal presence in the Massaciuccoli Lake basin,” Ecol. Inform., vol. 81, 2024, doi: 10.1016/j.ecoinf.2024.102644.

P. Prasad et al., “Integration of multi-temporal SAR data and robust machine learning models for improvement of flood susceptibility assessment in the southwest coast of India,” Appl. Comput. Geosci., vol. 24, 2024, doi: 10.1016/j.acags.2024.100189.

J. Hagenauer, H. Omrani, and M. Helbich, “Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany,” Int. J. Geogr. Inf. Sci., vol. 33, no. 7, pp. 1399–1419, 2019, doi: 10.1080/13658816.2019.1579333.

V. Lopez-vazquez, J. M. Lopez-guede, S. Marini, E. Fanelli, E. Johnsen, and J. Aguzzi, “Video image enhancement and machine learning pipeline for underwater animal detection and classification at cabled observatories,” Sensors, vol. 20, no. 3, 2020, doi: 10.3390/s20030726.

Á. Michelena et al., “A Fault-Detection System Approach for the Optimization of Warship Equipment Replacement Parts Based on Operation Parameters,” Sensors, vol. 23, no. 7, 2023, doi: 10.3390/s23073389.

L. Jian, S. Guo, and S. Yu, “Effect of Artificial Intelligence on the Development of China’s Wholesale and Retail Trade,” Sustain., vol. 15, no. 13, 2023, doi: 10.3390/su151310524.

N. Zaeri and R. R. Qasim, “Intelligent Wireless Sensor Network for Gas Classification Using Machine Learning,” IEEE Syst. J., vol. 17, no. 2, pp. 1765–1776, 2023, doi: 10.1109/JSYST.2023.3238357.

D. Jankovič, M. Šimic, and N. Herakovič, “A data-driven simulation and Gaussian process regression model for hydraulic press condition diagnosis,” Adv. Eng. Informatics, vol. 59, 2024, doi: 10.1016/j.aei.2023.102276.

J. Cao, K.-Y. Lam, L.-H. Lee, X. Liu, P. Hui, and X. Su, “Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence,” ACM Comput. Surv., vol. 55, no. 9, 2023, doi: 10.1145/3557999.

G. Coro, L. Sana, and P. Bove, “An open science automatic workflow for multi-model species distribution estimation,” Int. J. Data Sci. Anal., 2024, doi: 10.1007/s41060-024-00517-w.

F. Alonso-Sarría, C. Valdivieso-Ros, and F. Gomariz-Castillo, “Analysis of the hyperparameter optimisation of four machine learning satellite imagery classification methods,” Comput. Geosci., vol. 28, no. 3, pp. 551–571, 2024, doi: 10.1007/s10596-024-10285-y.

Z. Sun et al., “A review of Earth Artificial Intelligence,” Comput. Geosci., vol. 159, 2022, doi: 10.1016/j.cageo.2022.105034.

H. L. Dawson, O. Dubrule, and C. M. John, “Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification,” Comput. Geosci., vol. 171, 2023, doi: 10.1016/j.cageo.2022.105284.

L. G. S. Rocha, P. H. C. Kim, and K. C. Teixeira Vivaldini, “Performance analysis of path planning techniques for autonomous robots: A deep path planning analysis in 2D environments,” Int. J. Intell. Robot. Appl., vol. 7, no. 4, pp. 778–794, 2023, doi: 10.1007/s41315-023-00298-8.

D. Kellner, M. Lowin, and O. Hinz, “Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic,” Decis. Support Syst., vol. 172, 2023, doi: 10.1016/j.dss.2023.113983.

S. Shrimali, A. Pandey, and C. L. Chowdhary, “K-means clustering-based radio neutron star pulsar emission mechanism,” Recent Adv. Comput. Sci. Commun., vol. 14, no. 1, pp. 201–207, 2021, doi: 10.2174/2213275912666200129115401.

C. Redmond, M. Smit, I. Draganova, R. Corner-Thomas, D. Thomas, and C. Andrews, “The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (Canis familiaris): A Validation Study,” Sensors, vol. 24, no. 18, 2024, doi: 10.3390/s24185955.

Z. Y.-C. Liu et al., “Deep learning accurately predicts white shark locomotor activity from depth data,” Anim. Biotelemetry, vol. 7, no. 1, 2019, doi: 10.1186/s40317-019-0175-5.

DOI: http://dx.doi.org/10.12962/j25481479.v10i1.22308