GeoAI-Based Prediction of Yellowfin Tuna (Thunnus albacares) Potential Fishing Zones in the Western Sumatra Waters, Indonesia
Keywords:
Potential Fishing Zones (PFZ), Yellowfin tuna (Thunnus albacares), GeoAI and machine learning, Oceanographic variability, Environmental sustainability, Eastern Indian OceanAbstract
The western waters of Sumatra in the eastern Indian Ocean are highly productive fishing grounds for yellowfin tuna (Thunnus albacares). However, the dynamic oceanographic conditions of these waters pose challenges for reliable fisheries prediction. Therefore, this study aimed to identify key environmental drivers and predict Potential Fishing Zones (PFZs) using GeoArtificial Intelligence (GeoAI)-based machine learning (ML) framework integrating Guided Regularized Random Forest (GRRF) and Random Forest (RF). The predictive models were developed using multi-source oceanographic variables as predictors, including sea surface temperature (SST), chlorophyll-a (Chl-a), salinity, current velocity, and net primary productivity (NPP). The results of correlation analysis showed that there was a strong relationship between Chl-a and NPP (r = 0.87), indicating the central role of primary productivity in tuna habitat dynamics. Both models achieved high predictive accuracy, with GRRF slightly outperforming RF by reducing variable redundancy and improving interpretability. Tuna occurrence was strongly associated with moderate SST (29.2–29.8°C), high NPP (>350 mg C m⁻² day⁻¹), and zones influenced by the South Java Coastal Current (SJCC) and the Indonesian Throughflow (ITF). Spatially, PFZs were concentrated along the Mentawai slope and adjacent offshore waters, particularly during monsoon transition periods. These results showed the effectiveness of GeoAI-based methods for predicting PFZs and supported sustainable tuna fisheries management. Moreover, future studies should incorporate interannual climate variability (ENSO, IOD) and advanced deep learning frameworks to enhance spatiotemporal prediction.
Downloads
References
Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), 1-20. doi: 10.1080/15481603.2019.1650447 [Google Scholar] [CrossRef]
Ahmad, H., Jose, F., Bhuyan, M. S., Islam, M. N., & Dash, P. (2024). Seasonal influence of freshwater discharge on spa-tio-temporal variations in primary productivity, sea surface temperature, and euphotic zone depth in the nor-thern Bay of Bengal. Acta Oceanologica Sinica, 43(6), 1-14. doi: 10.1007/s13131-023-2254-y [Google Scholar] [CrossRef]
Al Iqromi, M. F., Harahap, A., Heltria, S., Ramdhani, F., Gelis, E. R. E., & Syarifuddin, H. (2025). Study on The Distribu-tion of Longtail Tuna (Thunnus Tonggol) Catch Based on Chlorophyll-A Using Aqua Modis Imagery in West Sumatera. Grouper, 16(1), 189-203. [Google Scholar]
Aoki, Y., Kitagawa, T., & Kiyofuji, H. (2025). Surfacing and diving behavior associated with thermal physiology in oceanic habitats of skipjack tuna (Katsuwonus pelamis) in the western north Pacific Ocean. Frontiers in Phy-siology, 16, 1462940. doi: 10.3389/fphys.2025.1462940 [Google Scholar] [CrossRef]
Atwa, W., Almazroi, A. A., & Ayub, N. (2024). Reliable renewable energy forecasting for climate change mitigation. PeerJ Computer Science, 10, e2067. doi: 10.7717/peerj-cs.2067 [Google Scholar] [CrossRef]
Boutayeb, A., Lahsen-Cherif, I., & El Khadimi, A. (2025). When Machine Learning Meets Geospatial Data: A Compre-hensive GeoAI Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18. doi: 10.1109/JSTARS.2025.3568715 [Google Scholar] [CrossRef]
Braun, C. D., Arostegui, M. C., Thorrold, S. R., Papastamatiou, Y. P., Gaube, P., Fontes, J., & Afonso, P. (2022). The functional and ecological significance of deep diving by large marine predators. Annual Review of Marine Science, 14(1), 129-159. doi: 10.1146/annurev-marine-032521-103517 [Google Scholar] [CrossRef]
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi: 10.1023/A:1010933404324 [Google Scholar] [CrossRef]
Brown, A., Purwati, P., & Isnaniah, I. (2023). Availability of fish catch production yellowfin tuna (Thunnus albacares) landed in the Bungus Ocean Fishery Port of West Sumatra Province. South East Asian Marine Sciences Jour-nal, 1(1), 1-7. [Google Scholar]
Chabalala, Y., Adam, E., & Ali, K. A. (2023). Exploring the effect of balanced and imbalanced multi-class distribution data and sampling techniques on fruit-tree crop classification using different machine learning classifiers. Geomatics, 3(1), 70-92. doi: 10.3390/geomatics3010004 [Google Scholar] [CrossRef]
Chen, X. (2022). Shoaling and migration of fish and their relationships with environment. In Theory and method of fis-heries forecasting. Singapore: Springer Nature Singapore, 39-85. doi: 10.1007/978-981-19-2956-4_3 [Google Scholar] [CrossRef]
Clift, P. D., Betzler, C., Clemens, S. C., Christensen, B., Eberli, G. P., France-Lanord, C., ... & Wan, S. (2022). A synthesis of monsoon exploration in the Asian marginal seas. Scientific Drilling, 31, 1-29. doi: 10.5194/sd-31-1-2022 [Google Scholar] [CrossRef]
Cloern, J. E., Foster, S. Q., & Kleckner, A. E. (2014). Phytoplankton primary production in the world's estuarine-coastal ecosystems. Biogeosciences, 11(9), 2477-2501. doi: 10.5194/bg-11-2477-2014 [Google Scholar] [CrossRef]
Dalabehara, H. B., & Sarma, V. V. S. S. (2021). Physical forcing controls spatial variability in primary production in the Indian Ocean. Deep Sea Research Part II: Topical Studies in Oceanography, 183, 104906. doi: 10.1016/j.dsr2.2020.104906 [Google Scholar] [CrossRef]
Debiyanti, D., Gunawan, D., & Budiyanto, S. (2025). Patterns of oceanographic factor distribution and tuna fishing po-tential: Spatial and temporal analysis. Remote Sensing Technology in Defense and Environment, 2(1), 60-77. doi: 10.61511/rstde.v2i1.2025.1196 [Google Scholar] [CrossRef]
Ducklow, H., Cimino, M., Dunton, K. H., Fraser, W. R., Hopcroft, R. R., Ji, R., ... & Sosik, H. M. (2022). Marine pelagic ecosystem responses to climate variability and change. BioScience, 72(9), 827-850. doi: 10.1093/biosci/biac050 [Google Scholar] [CrossRef]
Fadhilah, A., Leidonald, R., Susetya, I. E., & Dewinta, A. F. (2022). Analysis of the relationship between the ENSO phe-nomenon, Net Primary Productivity and catches of yellowfin tuna in Sibolga Waters, Indonesia. Biodiversitas Journal of Biological Diversity, 23(9). [Google Scholar]
Farhadpour, S., Warner, T. A., & Maxwell, A. E. (2024). Selecting and interpreting multiclass loss and accuracy as-sessment metrics for classifications with class imbalance: Guidance and best practices. Remote Sensing, 16(3), 533. doi: 10.3390/rs16030533 [Google Scholar] [CrossRef]
Fontaine, D. N., Marrec, P., Menden‐Deuer, S., Sosik, H. M., & Rynearson, T. A. (2025). Time series of phytoplankton net primary production reveals intense interannual variability and size‐dependent chlorophyll‐specific produc-tivity on a continental shelf. Limnology and Oceanography, 70(1), 203-216. doi: 10.1002/lno.12749 [Google Scholar] [CrossRef]
Franzè, G., Anderson, S. I., Kling, J. D., Wilburn, P., Hutchins, D. A., Litchman, E., ... & Menden‐Deuer, S. (2023). Inte-ractive effects of nutrients and temperature on herbivorous predation in a coastal plankton community. Limno-logy and Oceanography, 68, S144-S157. doi: 10.1002/lno.12289 [Google Scholar] [CrossRef]
Gao, S., Hu, Y., & Li, W. (2023). Introduction to geospatial artificial intelligence (GeoAI). Handbook of geospatial arti-ficial intelligence, 3-16. doi: 10.1201/9781003308423 [Google Scholar] [CrossRef]
Geerts, M., Vanden Broucke, S., & De Weerdt, J. (2024). GeoRF: a geospatial random forest. Data Mining and Knowledge Discovery, 38(6), 3414-3448. doi: 10.1007/s10618-024-01046-7 [Google Scholar] [CrossRef]
Gladju, J., Kamalam, B. S., & Kanagaraj, A. (2022). Applications of data mining and machine learning framework in aquaculture and fisheries: A review. Smart Agricultural Technology, 2, 100061. doi: 10.1016/j.atech.2022.100061 [Google Scholar] [CrossRef]
Hanif, M., Apichontrakul, S., & Razi, P. (2024). Surface deformation monitoring and forecasting of sinabung volcano using interferometry synthetic aperture radar and forest-based algorithm. Remote Sensing Applications: Society and Environment, 36, 101288. doi: 10.1016/j.rsase.2024.101288 [Google Scholar] [CrossRef]
Hanif, M., Apichontrakul, S., Suryani, R., & Ramadhan, R. (2026). Landslide susceptibility prediction on mount Marapi using Interferometric Synthetic aperture radar (INSAR) integrated with multi-model machine learning ap-proaches. Geography, Environment, Sustainability, 19(2), 29-40. doi: 10.24057/2071-9388-2026-4016 [Google Scholar] [CrossRef]
Hao, R., Zhao, Y., Zhang, S., & Deng, X. (2025). Deep Learning for Ocean Forecasting: A Comprehensive Review of Methods, Applications, and Datasets. IEEE Transactions on Cybernetics. doi: 10.1109/TCYB.2025.3539990 [Google Scholar] [CrossRef]
Heltria, S., Ramdhani, F., & Yuliardi, A. Y. (2024). Suitability Of Spatial-Temporal Habitat Of Yellowfin Tuna (Thun-nus Albacares) Using Maximum Entropy Model In West Sumatra. Marine Fisheries: Journal of Marine Fis-heries Technology and Management, 15(2), 147-158. doi: 10.29244/jmf.v15i2.52940 [Google Scholar] [CrossRef]
Hosen, B., Rahaman, M., Kumar, S., Sagar, L., & Akhtar, M. N. (2023). Leveraging artificial intelligence and big data for advanced spatial analytics and decision support systems in geography. Malaysian Applied Geography, 1(2), 62-67. doi: 10.26480/magg.02.2023.62.67 [Google Scholar] [CrossRef]
Iranzad, R., & Liu, X. (2025). A review of random forest-based feature selection methods for data science education and applications. International Journal of Data Science and Analytics, 20(2), 197-211. https://doi.org/10.1007/s41060-024-00509-w[Google Scholar] [CrossRef]
Islam, M. K., & Moontaha, T. J. (2025). Satellite-based monitoring of chlorophyll-a and sea surface temperature for identifying potential fishing zones (PFZs) for Hilsha fish: a case study in the Bay of Bengal. Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVII, 13666, 371-388. doi: 10.1117/12.3070008 [Google Scholar] [CrossRef]
Ismail, K. A., & Al-Shehhi, M. R. (2023). Applications of biogeochemical models in different marine environments: a review. Frontiers in Environmental Science, 11, 1198856. doi: 10.3389/fenvs.2023.1198856 [Google Scholar] [CrossRef]
Izquierdo-Verdiguier, E., & Zurita-Milla, R. (2020). An evaluation of Guided Regularized Random Forest for classifica-tion and regression tasks in remote sensing. International Journal of Applied Earth Observation and Geoin-formation, 88, 102051. doi: 10.1016/j.jag.2020.102051 [Google Scholar] [CrossRef]
John, S. S., Kulkarni, S. A., Begam, M. F., & Hiremath, A. (2025). Forecasting of Potential Fishing Zones in the Arabian Sea Using Satellite Imagery. In 2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE, 544-549. doi: 10.1109/IAICT65714.2025.11101450 [Google Scholar] [CrossRef]
Kawaguchi, O. (2024). Machine-learning and thresholding algorithms to automatically predict fishing effort of small-scale trawl fishery. Fisheries Science, 90(2), 123-137. doi: 10.1007/s12562-023-01734-1 [Google Scholar] [CrossRef]
Lattanzi, P., Vasapollo, C., Samarão, J., Galdelli, A., Mendo, T., Rufino, M., ... & Tassetti, A. N. (2026). Predicting fis-hing vs. not-fishing in small-scale fisheries: A sample vessel tracking dataset and a reproducible machine lear-ning approach. SoftwareX, 34, 102579. doi: 10.1016/j.softx.2026.102579 [Google Scholar] [CrossRef]
Lellouche, J. M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., ... & Le Traon, P. Y. (2018). Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1/12∘ high-resolution system. Ocean Science, 14(5), 1093-1126. doi: 10.5194/os-14-1093-2018 [Google Scholar] [CrossRef]
Letscher, R. T., Moore, J. K., Martiny, A. C., & Lomas, M. W. (2023). Biodiversity and stoichiometric plasticity increase pico‐phytoplankton contributions to marine net primary productivity and the biological pump. Global Bio-geochemical Cycles, 37(8), e2023GB007756. https://doi.org/10.1029/2023GB007756[Google Scholar] [CrossRef]
Li, J., Li, Y., Guo, Y., Li, G., & Wang, F. (2023). Decadal variability of sea surface salinity in the Southeastern Indian Ocean: Roles of local rainfall and the Indonesian throughflow. Frontiers in Marine Science, 9, 1097634. doi: 10.3389/fmars.2022.1097634 [Google Scholar] [CrossRef]
Lima, A. R., Garrido, S., Riveiro, I., Rodrigues, D., Angélico, M. M., Gonçalves, E. J., ... & Silva, G. (2022). Seasonal ap-proach to forecast the suitability of spawning habitats of a temperate small pelagic fish under a high-emission climate change scenario. Frontiers in Marine Science, 9, 956654. doi: 10.3389/fmars.2022.956654 [Google Scholar] [CrossRef]
Lisna, Febryanti H. R., Heltria S., Toni Y., Ramadan F., Ramdhani F., 2024 Estimation of the fishing ground potential based on oceanography parameters (GIS-based) of yellowfin tuna (Thunnus albacares) in West Sumatra waters. AACL Bioflux, 17(6):3175-3189.
Matsubara, N., Aoki, Y., Aoki, A., & Kiyofuji, H. (2024). Lower thermal tolerance restricts vertical distributions for ju-venile albacore tuna (Thunnus alalunga) in the northern limit of their habitats. Frontiers in Marine Science, 11, 1353918. [Google Scholar]
Miranda, F. M., Köhnecke, N., & Renard, B. Y. (2023). Hiclass: a python library for local hierarchical classification compatible with scikit-learn. Journal of Machine Learning Research, 24(29), 1-17. doi: 10.48550/arXiv.2112.06560 [Google Scholar] [CrossRef]
Nadeem, A., Rana, A. D., Batool, S. A., Azhar, A., Ishfaq, U. B. E., & Hameed, A. (2025). Satellite based assessment of potential fishing zones (PFZs) within the exclusive economic zone (EEZ) of Pakistan. Continental Shelf Research, 286, 105410. doi: 10.1016/j.csr.2025.105410 [Google Scholar] [CrossRef]
Navarro, J., Lonin, S., Linero-Cueto, J., & Romero-Balcucho, C. (2025). Hydrodynamic Modelling of the Guajira Up-welling System (Colombia). Applied Sciences, 15(20), 11000. doi: 10.3390/app152011000 [Google Scholar] [CrossRef]
Nóbrega, M. F., de Lira, M. G., Oliveira, M. A., & Oliveira, J. E. L. (2023). Interactions between oceanographic variables and population structure of the yellowfin tuna Thunnus albacares (Bonnaterre, 1788) in the Western Central Atlantic. Fisheries Oceanography, 32(2), 213-228. doi: 10.1111/fog.12624 [Google Scholar] [CrossRef]
Ni, B., & Wang, X. (2024). Geographic research in the AI era: Applications, methods, and prospects. Geographical Research Bulletin, 3, 103-124. doi: 10.50908/grb.3.0_103 [Google Scholar] [CrossRef]
Nikparvar, B., & Thill, J. C. (2021). Machine learning of spatial data. ISPRS International Journal of Geo-Information, 10(9), 600. doi: 10.3390/ijgi10090600 [Google Scholar] [CrossRef]
Nurholis., Gaol, J. L., Syah, A. F., & Dewi, A. K. (2020). GIS-based spatio-temporal analysis on yellow fin tuna catch in Eastern Indian Ocean off Sumatera. IOP Conference Series: Earth and Environmental Science, 429(1), 012041. doi: 10.1088/1755-1315/429/1/012041 [Google Scholar] [CrossRef]
Nurholis., Sukoraharjo, S. S., Lumban-Gaol, J., & Syah, A. F. (2021). Yellowfin tuna fishing dynamic during low IOD positive and negative at eastern Indian ocean; study case at western waters of sumatera. In IOP Conference Se-ries: Earth and Environmental Science, 695(1), 012010. doi: 10.1088/1755-1315/695/1/012010 [Google Scholar] [CrossRef]
Panzeri, D., Libralato, S., Carlucci, R., Cipriano, G., Bitetto, I., Spedicato, M. T., ... & Solidoro, C. (2021). Defining a procedure for integrating multiple oceanographic variables in ensemble models of marine species distribution. In 2021 International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (Metro-Sea). IEEE, 360-365. doi: 10.1109/MetroSea52177.2021.9611559 [Google Scholar] [CrossRef]
Pourzangbar, A., Jalali, M., & Brocchini, M. (2023). Machine learning application in modelling marine and coastal phenomena: a critical review. Frontiers in Environmental Engineering, 2, 1235557. doi: 10.3389/fenve.2023.1235557 [Google Scholar] [CrossRef]
Purba, N. P., Pranowo, W. S., Ndah, A. B., & Nanlohy, P. (2021). Seasonal variability of temperature, salinity, and sur-face currents at 0 latitude section of Indonesia seas. Regional Studies in Marine Science, 44, 101772. doi: 10.1016/j.rsma.2021.101772 [Google Scholar] [CrossRef]
Rahaman, H., Srinivasu, U., Panickal, S., Durgadoo, J. V., Griffies, S. M., Ravichandran, M., ... & Wang, Q. (2020). An assessment of the Indian Ocean mean state and seasonal cycle in a suite of interannual CORE-II simulations. Ocean Modelling, 145, 101503. doi: 10.1016/j.ocemod.2019.101503 [Google Scholar] [CrossRef]
Sahib, W. M., Alhuseen, Z. A. A., Saeedi, I. D. I., Abdulkadhem, A. A., & Ahmed, A. (2025). Leveraging machine lear-ning for enhanced cybersecurity: an intrusion detection system. Service Oriented Computing and Applica-tions, 19(2), 107-124. doi: 10.1007/s11761-024-00435-6 [Google Scholar] [CrossRef]
Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69-79. doi: 10.58496/BJML/2024/007 [Google Scholar] [CrossRef]
Sambah, A. B., Muamanah, A., Harlyan, L. I., Lelono, T. D., Iranawati, F., & Sartimbul, A. (2021). Sea surface tempera-ture and chlorophyll-a distribution from Himawari satellite and its relation to yellowfin tuna in the Indian Ocean. Aquaculture, Aquarium, Conservation & Legislation, 14(2), 897-909. http://www.bioflux.com.ro/docs/2021.897-909.pdf[Google Scholar]
Sarangi, R. K., Jishad, M., Sharma, R., Das, A., Mali, K., Ramalingam, L., ... & Prakash, C. (2024). Multiple ocean para-meter-based potential fishing zone (PFZ) location generation and validation in the Western Bay of Bengal. En-vironmental Monitoring and Assessment, 196(1), 98. doi: 10.1007/s10661-023-12259-6 [Google Scholar] [CrossRef]
Sari, N. R., Hartoko, A., & Fitri, A. D. (2024). Potential big eye tuna (Thunnus obesus) fishing zone in western Sumatra Sea, Indonesia based on Sub-surface Temperature. International Journal of Multidisciplinary Research and Growth Evaluation, 5(2). doi: 10.54660/.IJMRGE.2024.5.2.72-76 [Google Scholar] [CrossRef]
Satar, M. N., Marshal, W., & Akhir, M. F. (2024). The Effects of Climate Change on Ocean Upwelling and Productivity in Regional Seas. In Coastal Sustainability: Insights from Southeast Asia and Beyond. Cham: Springer Nature Switzerland, 21-49. doi: 10.1007/978-3-031-75749-5_2 [Google Scholar] [CrossRef]
Siddique, I. (2024). Machine learning empowered geographic information systems: Advancing Spatial analysis and de-cision making. World Journal of Advanced Research and Reviews, 22(1), 10-30574. doi: 10.30574/wjarr.2024.22.1.1200 [Google Scholar] [CrossRef]
Singh, Y., Pandey, V. K., Mishra, S. K., & Kushwaha, P. (2024). Understanding environmental drivers and trends in chlorophyll-a and related factors across equatorial Indian Ocean regions: implications for marine ecosystem dynamics and vulnerability. Marine Biology Research, 20(9-10), 365-379. doi: 10.1080/17451000.2024.2371327 [Google Scholar] [CrossRef]
Siregar, E. S. Y., Simbolon, D., Wahju, R. I., Yulianto, I., & Siregar, V. P. (2025). Yellowfin Tuna (Thunnus Albacares) Fisheries Sustainability: A Weight-Length Analysis In The West Sumatera Waters. Jurnal Teknologi Perikanan Dan Kelautan, 16(3), 282-293. doi: 10.24319/jtpk.16.282-293 [Google Scholar] [CrossRef]
Smoliński, S., & Radtke, K. (2017). Spatial prediction of demersal fish diversity in the Baltic Sea: comparison of ma-chine learning and regression-based techniques. ICES Journal of Marine Science, 74(1), 102-111. doi: 10.1093/icesjms/fsw136 [Google Scholar] [CrossRef]
Song, T., Pang, C., Hou, B., Xu, G., Xue, J., Sun, H., & Meng, F. (2023a). A review of artificial intelligence in marine science. Frontiers in Earth Science, 11, 1090185. doi: 10.3389/feart.2023.1090185 [Google Scholar] [CrossRef]
Song, L., Li, T., Zhang, T., Sui, H., Li, B., & Zhang, M. (2023b). Comparison of machine learning models within diffe-rent spatial resolutions for predicting the bigeye tuna fishing grounds in tropical waters of the Atlantic Ocean. Fisheries Oceanography, 32(6), 509-526. doi: 10.1111/fog.12643 [Google Scholar] [CrossRef]
Srivastava, N., & Saxena, N. (2023). Applications of artificial intelligence and machine learning in geospatial data. In Emerging Trends, Techniques, and Applications in Geospatial Data Science. IGI Global, 196-219. doi: 10.4018/978-1-6684-7319-1.ch010 [Google Scholar] [CrossRef]
Talebi, H., Peeters, L. J., Otto, A., & Tolosana-Delgado, R. (2022). A truly spatial random forests algorithm for geos-cience data analysis and modelling. Mathematical Geosciences, 54(1), 1-22. doi: 10.1007/s11004-021-09946-w [Google Scholar] [CrossRef]
Utari, P. A., Setiabudidaya, D., Khakim, M., & Iskandar, I. (2019). Dynamics of the South Java Coastal Current revealed by RAMA observing network. Terrestrial, Atmospheric & Oceanic Sciences, 30(2). doi: 10.3319/TAO.2018.12.14.01 [Google Scholar] [CrossRef]
Vinston Raja, R., & Ashok Kumar, K. (2022). Fisher Scoring with Condition-Based Ensemble Supervised Learning Classification Technique for Prediction in PFZ. Journal of Uncertain Systems, 15(03), 2241009. doi: 10.1142/S1752890922410094 [Google Scholar] [CrossRef]
Waller, M. J., Humphries, N. E., Womersley, F. C., Loveridge, A., Jeffries, A. L., Watanabe, Y., ... & Sims, D. W. (2024). The vulnerability of sharks, skates, and rays to ocean deoxygenation: Physiological mechanisms, behavioral responses, and ecological impacts. Journal of Fish Biology, 105(2), 482-511. doi: 10.1111/jfb.15830 [Google Scholar] [CrossRef]
Wessel, P., Luis, J. F., Uieda, L. A., Scharroo, R., Wobbe, F., Smith, W. H., & Tian, D. (2019). The generic mapping tools version 6. Geochemistry, Geophysics, Geosystems, 20(11), 5556-5564. doi: 10.1029/2019GC008515 [Google Scholar] [CrossRef]
Xing, Q., Yu, H., Wang, H., Ito, S. I., & Chai, F. (2023). Mesoscale eddies modulate the dynamics of human fishing acti-vities in the global midlatitude ocean. Fish and Fisheries, 24(4), 527-543. doi: 10.1111/faf.12742 [Google Scholar] [CrossRef]
Yuliardi, A. Y., Afiyah, N. N., Napitupulu, G., Munandar, B., Haryati, A., Husni, I. A., & Koropitan, A. F. (2026). Ocea-nographic Variability and its Association with Fisheries Productivity in Lamongan, Indonesia. Thalassas: An International Journal of Marine Sciences, 42(2), 57. doi: 10.1007/s41208-026-01088-2 [Google Scholar] [CrossRef]
Zhang, J., Fan, D., He, H., Xiao, B., Xiong, Y., & Shi, J. (2023). Forecasting Albacore (Thunnus alalunga) fishing grounds in the South Pacific based on machine learning algorithms and ensemble learning model. Applied Sciences, 13(9), 5485. doi: 10.3390/app13095485 [Google Scholar] [CrossRef]
Zhang, Y., Liu, J., & Shen, W. (2022a). A review of ensemble learning algorithms used in remote sensing applications. Applied Sciences, 12(17), 8654. https://doi.org/10.3390/app12178654[Google Scholar] [CrossRef]
Zhang, Z., Wang, J., & Yuan, D. (2022b). Mixed layer salinity balance in the eastern tropical Indian Ocean. Journal of Geophysical Research: Oceans, 127(6), e2021JC018229. doi: 10.1029/2021JC018229 [Google Scholar] [CrossRef]
Zhao, Q., Peng, S., Wang, J., Li, S., Hou, Z., & Zhong, G. (2024). Applications of deep learning in physical ocea-nography: a comprehensive review. Frontiers in Marine Science, 11, 1396322. doi: 10.3389/fmars.2024.1396322 [Google Scholar] [CrossRef]
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Amir Yarkhasy Yuliardi, Muhammad Hanif, Septy Heltria, Diah Ayu Rahmalia, Gandhi Napitupulu
This work is licensed under a Creative Commons Attribution 4.0 International License.
