DOI: https://doi.org/10.14710/ijfst.22.3.241-248

AN ARTIFICIAL NEURAL NETWORK APPROACH FOR PREDICTING PELAGIC FISH HABITATS IN FISHERIES MANAGEMENT AREAS (WPP) 573 USING MULTI-PARAMETER SATELLITE DATA

*Rosyid Paundra Gamawan  -  Master Program in Remote Sensing, Faculty of Geography, Gadjah Mada University, Indonesia, Indonesia
Nur Mohammad Farda orcid scopus  -  Department of Geographic Information Science, Faculty of Geography, Gadjah Mada University, Indonesia, Indonesia
Prima Widayani orcid scopus  -  Department of Geographic Information Science, Faculty of Geography, Gadjah Mada University, Indonesia, Indonesia
Favian Mafazi Giska Putra orcid scopus  -  Research Center for Hydrodynamics Technology, National Research and Innovation Agency, Indonesia, Indonesia
Widia Pangestika orcid scopus  -  Nutrition Study Program, Faculty of Medicine and Health Sciences, Sultan Ageng Tirtayasa University, Indonesia, Indonesia
Ailsa Afra Mawarid orcid scopus  -  Food Technology Laboratory, Integrated Laboratory Unit, Diponegoro University, Indonesia, Indonesia
Irfani Anis  -  Master Program in Remote Sensing, Faculty of Geography, Gadjah Mada University, Indonesia, Indonesia

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Abstract

Fisheries Management Area (WPP) 573 is one of the upwelling regions of the Indian Ocean supporting both neritic small pelagic and oceanic pelagic fisheries, and is affected by complex climate variability. This research modeled spatial probabilities of fish pelagic habitat using an optimized shallow architecture of a Feedforward Network, specifically a Multi-layer Perceptron integrated with oceanographic satellite data. This research utilized 492 validated fishing coordinates from Global Fishing Watch (GFW) collected throughout 2025 as presence labels. At the same time, oceanographic variables included sea surface temperature, distance to the nearest coral reef, chlorophyll-a, salinity, sea surface elevation, current velocity, and bathymetry. To prioritize model generalization and mitigate overfitting, this research implemented the MLP with a single hidden layer containing three neurons, optimized via the L-BFGS algorithm with strong regularization. The final model with spatially- constrained pseudo-absences, environmental distance filtering obtained a True Skill Statistic (TSS) of 0.68, a 5-fold cross-validation accuracy of 83.95% and a presence precision of 95%, indicating that the model possess a good ability to discriminate among habitats and it is robust to variation. The permutation importance highlighted SST as the strongest predictor (0.1386), followed by salinity (0.0976) and current velocity (0.0866). Dynamic habitat probability maps generated reveal fish catch potential zones that correspond to the biophysical conditions of the water mass. In summary, this study demonstrates that MLP-based prediction, reinforced by stringent parameter optimization, is a very suitable approach to identify pelagic fish habitats, thus assisting in fishing operations and sustainable fishery management in WPP 573.

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Keywords: Pelagic Fish Habitat, Multilayer Perceptron, Satellite Oceanography, Global Fishing Watch, WPP 573
Funding: This research is part of projects titled "Establishment of the Integrated Ocean Fisheries Technology Training Center and the Enhancement of Capacity Building in Indonesia (1192000-2023-006)" which are funded by the Ministry of Oceans and Fisheries, Korea.

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  1. Abrahams, A., Schlegel, R., and Smit, A. (2021). Variation and change of upwelling dynamics detected in the world's eastern boundary upwelling systems. Frontiers in Marine Science, 8, 626411
  2. Alotaibi, E., and Nassif, N. (2024). Artificial intelligence in environmental monitoring: in-depth analysis. Discover Artificial Intelligence, 4, 1981
  3. Ashrafi, T. A., and Abe, K. (2021). Intra- and inter-temporal effort allocation and profit-maximizing strategy of trawl fishery. Ices Journal of Marine Science
  4. Babbel, B., Parrish, C., and Magruder, L. (2021). ICESat-2 elevation retrievals in support of satellite-derived bathymetry for global science applications. Geophysical Research Letters, 48, e2020GL090629
  5. Bald, L., Gottwald, J., and Zeuss, D. (2023). spatialMaxent: Adapting species distribution modeling to spatial data. Ecology and Evolution, 13
  6. Banda, T., and Kumarasamy, M. (2024). Artificial neural network (ANN)-based water quality index (WQI) for assessing spatiotemporal trends in surface water quality—A case study of South African river basins. Water, 16, 111485
  7. Barth, A., Álvera-Azcárate, A., Troupin, C., and Beckers, J. (2022). DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations. Geoscientific Model Development, 15, 2183–2202
  8. Behivoke, F., Étienne, M., Guitton, J., Randriatsara, R., Ranaivoson, E., and Léopold, M. (2021). Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests. Ecological Indicators, 121, 107321
  9. Bejani, M., and Ghatee, M. (2021). A systematic review on overfitting control in shallow and deep neural networks. Artificial Intelligence Review, 54, 6391–6438
  10. Caballero, I., and Stumpf, R. (2023). Confronting turbidity, the major challenge for satellite-derived coastal bathymetry. Science of the Total Environment, 881, 161898
  11. Cai, L., Kreft, H., Taylor, A., Denelle, P., Schrader, J., Essl, F., ... Weigelt, P. (2022). Global models and predictions of plant diversity based on advanced machine learning techniques. New Phytologist, 237, 1432–1445
  12. Chen, J., Gong, X., Guo, X., Xing, K., Lu, K., Gao, H., and Gong, X. (2022). Improved perceptron of subsurface chlorophyll maxima by a deep neural network: A case study with BGC-Argo float data in the Northwestern Pacific Ocean. Remote Sensing, 14, 632
  13. Coro, G., Sana, L., and Bove, P. (2024). An open science automatic workflow for multi-model species distribution estimation. International Journal of Data Science and Analytics, 20, 1131 - 1150
  14. Dahms, C., and Killen, S. (2023). Temperature change effects on marine fish range shifts: A meta-analysis of ecological and methodological predictors. Global Change Biology, 29, 4459–4479
  15. Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F., and Joly, A. (2021). Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Computational Biology, 17, e1008856
  16. Descombes, P., Chauvier, Y., Brun, P., Righetti, D., Wüest, R. O., Karger, D., Zurell, D., and Zimmermann, N. (2022). Strategies for sampling pseudo-absences for species distribution models in complex mountainous terrain. bioRxiv
  17. Diogoul, N., Brehmer, P., Demarcq, H., Ayoubi, S., Thiam, A., Sarré, A., ... Perrot, Y. (2021). On the robustness of an eastern boundary upwelling ecosystem exposed to multiple stressors. Scientific Reports, 11, 1–13
  18. Embury, O., Merchant, C., Good, S., Rayner, N., Høyer, J., Atkinson, C., ... Donlon, C. (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data, 11, 326
  19. Galappaththi, E., Susarla, V., Loutet, S., Ichien, S., Hyman, A., and Ford, J. (2021). Climate change adaptation in fisheries. Fish and Fisheries, 22, 1141–1159
  20. Guiet, J., Bianchi, D., Scherrer, K., Heneghan, R., and Galbraith, E. (2024). BOATSv2: new ecological and economic features improve simulations of high seas catch and effort. Geoscientific Model Development
  21. Jang, E., Kim, Y., Im, J., Park, Y., and Sung, T. (2022). Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learning. Remote Sensing of Environment, 273, 112980
  22. Jemeļjanova, M., Kmoch, A., and Uuemaa, E. (2024). Adapting machine learning for environmental spatial data - A review. Ecological Informatics, 81, 102634
  23. Jiang, B., and Zhou, W. (2025). Fishing operation type recognition based on multi-branch convolutional neural network using trajectory data. PeerJ Computer Science, 11, e3020
  24. Kadagi, N., Wambiji, N., Fennessy, S., Allen, M., and Ahrens, R. (2021). Challenges and opportunities for sustainable development and management of marine recreational and sport fisheries in the Western Indian Ocean. Marine Policy, 124, 104351
  25. Kalina, J., Tumpach, J., and Holeňa, M. (2022). On combining robustness and regularization in training multilayer perceptrons over small data. Proceedings of the 2022 IJCNN, 1–8
  26. Karp, M., Brodie, S., Smith, J., Richerson, K., Selden, R., Liu, O., ... Jacox, M. (2022). Projecting species distributions using fishery-dependent data. Fish and Fisheries, 24, 212–231
  27. Kass, J., Muscarella, R., Galante, P., Bohl, C., Pinilla-Buitrago, G., Boria, R., ... Anderson, R. (2021). ENMeval 2.0: Redesigned for customizable and reproducible modeling of species' niches and distributions. Methods in Ecology and Evolution, 12, 1602–1608
  28. Kerry, C., Exeter, O. M., and Witt, M. (2022). Monitoring global fishing activity in proximity to seamounts using automatic identification systems. Fish and Fisheries
  29. Koldasbayeva, D., Tregubova, P., Gasanov, M., Zaytsev, A., Petrovskaia, A., and Burnaev, E. (2024). Challenges in data-driven geospatial modeling for environmental research and practice. Nature Communications, 15, 1–14
  30. Koropitan, A., Kholilullah, I., and Yusfiandayani, R. (2021). Modeling Mackerel Tuna (Euthynnus affinis) Habitat in Southern Coast of Java: Influence of Seasonal Upwelling and Negative IOD. HAYATI Journal of Biosciences
  31. Lee, W., Song, J.-W., Yoon, S., and Jung, J.-M. (2022). Spatial Evaluation of Machine Learning-Based Species Distribution Models for Prediction of Invasive Ant Species Distribution. Applied Sciences, 12
  32. Lima, A., Baltazar-Soares, M., Garrido, S., Riveiro, I., Carrera, P., Piecho-Santos, A., ... Silva, G. (2022). Forecasting shifts in habitat suitability across the distribution range of a temperate small pelagic fish under different scenarios of climate change. Science of the Total Environment, 804, 150167
  33. Lumban-Gaol, J., Siswanto, E., Mahapatra, K., Natih, N. M., Nurjaya, I., Hartanto, M. T., Maulana, E., Adrianto, L., Rachman, H. A., Osawa, T., Rahman, B. M. K., and Permana, A. (2021). Impact of the Strong Downwelling (Upwelling) on Small Pelagic Fish Production during the 2016 (2019) Negative (Positive) Indian Ocean Dipole Events in the Eastern Indian Ocean off Java. Climate, 9
  34. Mandal, S., Susanto, R., and Ramakrishnan, B. (2022). On Investigating the Dynamical Factors Modulating Surface Chlorophyll-a Variability along the South Java Coast. Remote Sensing, 14, 1745
  35. McCarthy, M., Otis, D., Hughes, D., and Müller-Karger, F. (2022). Automated high-resolution satellite-derived coastal bathymetry mapping. International Journal of Applied Earth Observation and Geoinformation, 107, 102693
  36. McDonald, G. G., Bone, J., Costello, C., Englander, G., and Raynor, J. (2024). Global expansion of marine protected areas and the redistribution of fishing effort. Proceedings of the National Academy of Sciences, 121
  37. Milanesi, P., Della Rocca, F., & Robinson, R. A. (2020). Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models. Ecology and Evolution, 10(2), 1087–1092
  38. Narvekar, J., Chowdhury, R., Gaonkar, D., Kumar, P., and Kumar, P. (2021). Observational evidence of stratification control of upwelling and pelagic fishery in the eastern Arabian Sea. Scientific Reports, 11, 7523
  39. Paolo, F., Kroodsma, D., Raynor, J., Hochberg, T., Davis, P., Cleary, J., ... Halpin, P. (2024). Satellite mapping reveals extensive industrial activity at sea. Nature, 625, 85–91
  40. Park, Y., and Lek, S. (2016). Artificial neural networks: Multilayer perceptron for ecological modeling. In S. E. Jørgensen (Ed.), Developments in Environmental Modelling (Vol. 28, pp. 123–140). Elsevier
  41. Pickens, B., Carroll, R., Schirripa, M., Forrestal, F., Friedland, K., and Taylor, J. (2021). A systematic review of spatial habitat associations and modeling of marine fish distribution: A guide to predictors, methods, and knowledge gaps. PLoS ONE, 16, e0251818
  42. Ramampiandra, E., Scheidegger, A., Wydler, J., and Schuwirth, N. (2023). A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation. Ecological Modelling, 481, 110353
  43. Rufener, M., Kristensen, K., Nielsen, J., and Bastardie, F. (2021). Bridging the gap between commercial fisheries and survey data to model the spatiotemporal dynamics of marine species. Ecological Applications, 31, e02453
  44. Rustam, F., Ishaq, A., Kokab, S., Díez, I., Mazón, J., Rodríguez, C., and Ashraf, I. (2022). An artificial neural network model for water quality and water consumption prediction. Water, 14, 213359
  45. Sardenne, F., Munaron, J. M., Geja, Y., Jaffrezic, E., Vagner, M., Pecquerie, L., and van der Lingen, C. (2026). Long-chain omega-3 fatty acids and metallic elements in small pelagic fish from South Africa from a human consumption perspective. Journal of Food Composition and Analysis, 152, 109018
  46. Sarré, A., Demarcq, H., Keenlyside, N., Krakstad, J., Ayoubi, S., Jeyid, A., ... Brehmer, P. (2024). Climate change impacts on small pelagic fish distribution in Northwest Africa: trends, shifts, and risk for food security. Scientific Reports, 14, 10565
  47. Selvaraj, J., Rosero-Henao, L. V., and Cifuentes-Ossa, M. A. (2022). Projecting future changes in distributions of small-scale pelagic fisheries of the southern Colombian Pacific Ocean. Heliyon, 8
  48. Simanjuntak, F., and Lin, T.-H. (2022). Monsoon Effects on Chlorophyll-a, Sea Surface Temperature, and Ekman Dynamics Variability along the Southern Coast of Lesser Sunda Islands and Its Relation to ENSO and IOD Based on Satellite Observations. Remote Sensing, 14, 1682
  49. Simonetti, D., Pimple, U., Langner, A., & Marelli, A. (2021). Pan-tropical Sentinel-2 cloud-free annual composite datasets. Data in Brief, 39, 107488
  50. Soltanolkotabi, M., Javanmard, A., and Lee, J. (2017). Theoretical insights into the optimization landscape of over-parameterized shallow neural networks. IEEE Transactions on Information Theory, 65, 742–769
  51. Srinivasu, P., Lakshmi, J., Gudipalli, A., Narahari, S., Shafi, J., Woźniak, M., and Ijaz, M. (2024). XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer. Scientific Reports, 14, 27620
  52. Stacey, N., Gibson, E., Loneragan, N., Warren, C., Wiryawan, B., Adhuri, D., ... Fitriana, R. (2021). Developing sustainable small-scale fisheries livelihoods in Indonesia: Trends, enabling and constraining factors, and future opportunities. Marine Policy, 132, 104654
  53. Vinayachandran, P., Masumoto, Y., Roberts, M., Hugget, J., Halo, I., Chatterjee, A., ... Hood, R. (2021). Reviews and syntheses: Physical and biogeochemical processes associated with upwelling in the Indian Ocean. Biogeosciences, 18, 5967–6013
  54. Wang, S., Shin, M., and Bai, R. (2023). Generative Quantile Regression with Variability Penalty. Journal of Computational and Graphical Statistics, 33, 1202 - 1213
  55. Welch, H., Clavelle, T., White, T., Cimino, M., Van Osdel, J., Hochberg, T., ... Hazen, E. (2022). Hot spots of unseen fishing vessels. Science Advances, 8, eabq2109
  56. Wu, J., and Wang, Z. (2022). A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water, 14, 040610
  57. Zainuddin, M., Safruddin, S., Farhum, A., Budimawan, B., Hidayat, R., Selamat, M., ... Ihsan, Y. (2023). Satellite-based ocean color and thermal signatures defining habitat hotspots and the movement pattern for commercial skipjack tuna in Indonesia Fisheries Management Area 713, Western Tropical Pacific. Remote Sensing, 15, 1268
  58. Zbinden, R., Van Tiel, N., Kellenberger, B., Hughes, L., and Tuia, D. (2024). On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning. ArXiv, abs/2401.02989
  59. Zhou, W., Yan, Z., and Zhang, L. (2024). A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction. Scientific Reports, 14, 55243
  60. Zhu, M., Wang, J., Yang, X., Zhang, L., Ren, H., Wu, B., and Ye, L. (2022). A review of the application of machine learning in water quality evaluation. Eco-Environment and Health, 1, 107–116

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