Sustainable Marine Structures

Data‑Driven Environmental Monitoring Using Autonomous Underwater Vehicles: Adaptive Sampling in The Al Hoceima Marine Protected Area

Hasna Bouazzati

Research Laboratory in Applied and Marine Geosciences, Geotechnics, and Geohazards (LR3G), Faculty of Sciences, Abdelmalek Essaadi University, Tetouan 93030, Morocco

Asma Damghi

Research Laboratory in Applied and Marine Geosciences, Geotechnics, and Geohazards (LR3G), Faculty of Sciences, Abdelmalek Essaadi University, Tetouan 93030, Morocco

Xiang Gao

National Deep Sea Center of China.

Souhail Karim

Research Laboratory in Applied and Marine Geosciences Research and Development, Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Al Hoceima 32003, Morocco National Park of Al Hoceima, Head Ofϔice, Al Hoceima 32000, Morocco

Abdelmounim El M’rini

Research Laboratory in Applied and Marine Geosciences, Geotechnics, and Geohazards (LR3G), Faculty of Sciences, Abdelmalek Essaadi University, Tetouan 93030, Morocco

DOI: https://doi.org/10.36956/sms.v7i3.2162

Received: 16 May 2025 | Revised: 28 May 2025 | Accepted: 18 June 2025 | Published:01 July 2025

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Abstract

In this research, we examine how the Al Hoceima Marine Protected Area (MPA), located in the southwest Mediterranean Sea, can be effectively monitored using the SeaExplorer glider—an advanced autonomous underwater vehicle (AUV) designed for long-duration oceanographic missions. The study focuses on the glider’s ability to simultaneously observe a variety of environmental parameters, including temperature, conductivity, oxygen, and chlorophyll, during its deployment across multiple transects. The primary objective of the mission is to improve understanding of the vertical thermal structure and seasonal dynamics of the water column in this ecologically significant region. To achieve this, we apply Gaussian Process (GP) regression techniques to the glider-derived temperature data. This statistical method enables the smoothing and interpolation of irregularly spaced in situ measurements, thereby improving the visibility and interpretation of stratification patterns throughout the water column. Although the glider followed a predetermined course, the data-driven analysis suggests that adaptive sampling strategies—such as adjustments based on real-time outliers—could be valuable in future missions. Our results, which show distinct thermal layering and seasonal variability, are crucial for informing ecosystem function assessments and climate resilience planning. This study also discusses how integrating machine learning into glider-based monitoring could enhance MPA observation systems and promote adaptive, evidence-based management.

Keywords: Marine Protected Area; SeaExplorer Glider; Gaussian Processes; Remote Sensing

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References

[1] Benedetti-Cecchi, L., Bates, A.E., Strona, G., et al., 2024. Marine protected areas promote stability of reef fish communities under climate warming. Nature Communications. 15(1), 1822–1832. DOI: https://doi.org/10.1038/s41467-024-44976-y

[2] Bouazzati, H., Damghi, A., El M'rini, A., et al., 2024. Water quality and environmental resilience to climate change: A comprehensive analysis of the Al Hoceima Marine Protected Area. Journal of Coastal Research. 113(SI), 1049–1053. DOI: https://doi.org/10.2112/JCR-SI113-205.1

[3] Dar, A.A., Chen, Z., Sardar, M.F., et al., 2024. Navigating the nexus: Climate dynamics and microplastics pollution in coastal ecosystems. Environmental Research. 252(3), 118971. DOI: https://doi.org/10.1016/j.envres.2024.118971

[4] Cauchy, P., 2024. Marine soundscape monitoring from underwater autonomous vehicles—passive acoustic monitoring gliders. Journal of the Acoustical Society of America. 155(3_Supplement), A184–A184. DOI: https://doi.org/10.1121/10.0027249

[5] Lopazanski, C., Foshay, B., Couture, J.L., et al., 2023. Principles for climate resilience are prevalent in marine protected area management plans. Conservation Letters. 16(3), e12972. DOI: https://doi.org/10.1111/conl.12972

[6] Pampalone, V., Milici, B., 2015. High spatial resolution mapping of water quality and bathymetry with an autonomous underwater vehicle. AIP Conference Proceedings. 1702(1), 180006. DOI: https://doi.org/10.1063/1.4938955

[7] Davis, A., Paneerselvam, S., 2023. Design and Development of AUV for Coral Reef Inspection and Geotagging Using CV/ML. In: Bhattacharya, A., Dutta, S., Dutta, P., et al. (eds.). Advances in Intelligent Systems and Computing. Springer: Singapore. pp. 595–610. DOI: https://doi.org/10.1007/978-981-99-0550-8_47

[8] Seto, M.L. (ed.), 2013. Marine Robot Autonomy. Springer: New York, NY, USA. DOI: https://doi.org/10.1007/978-1-4614-5659-9

[9] Ferrari, R., Marzinelli, E.M., Ayroza, C.R., et al., 2018. Large-scale assessment of benthic communities across multiple marine protected areas using an autonomous underwater vehicle. PLOS ONE. 13(3), e0193711. DOI: https://doi.org/10.1371/journal.pone.0193711

[10] Liu, Y., Anderlini, E., Wang, S., et al., 2022. Ocean explorations using autonomy: Technologies, strategies and applications. In: Su, S., Wang, N. (eds.). Offshore Robotics. Springer: Singapore. pp. 35–58. DOI: https://doi.org/10.1007/978-981-16-2078-2_2

[11] Llewellyn, L.E., Bainbridge, S.J., 2015. Getting up close and personal: The need to immerse autonomous vehicles in coral reefs. Proceedings of the OCEANS 2015 - MTS/IEEE Washington; 19–22 October 2015; Washington, DC, USA. pp. 1–9. DOI: https://doi.org/10.23919/OCEANS.2015.7404565

[12] Rudnick, D.L., Davis, R.E., Eriksen, C.C., et al., 2004. Underwater gliders for ocean research. Marine Technology Society Journal. 38(2), 73–84. DOI: https://doi.org/10.4031/002533204787522703

[13] International Union for Conservation of Nature (IUCN), 2012. Marine Protected Areas: Global Experience. IUCN: Gland, Switzerland. Available from: https://portals.iucn.org/library/efiles/documents/2010-053.pdf (accessed 15 March 2025).

[14] Von Oppeln-Bronikowski, N., de Young, B., Belzile, M., et al., 2023. Best practices for operating underwater gliders in Atlantic Canada. Frontiers in Marine Science. 10, 1108326. DOI: https://doi.org/10.3389/fmars.2023.1108326

[15] Puillat, I., Farcy, P., Durand, D., Karlson, B., Petihakis, G., Seppälä, J., & Sparnocchia, S., 2016. Progress in marine science supported by European joint coastal observation systems: The JERICO‑RI research infrastructure. Journal of Marine Systems, 162, 1–3. DOI: https://doi.org/10.1016/j.jmarsys.2016.06.004

[16] Testor, P., de Young, B., Rudnick, D.L., et al., 2019. OceanGliders: A component of the integrated GOOS. Frontiers in Marine Science. 6, 422. DOI: https://doi.org/10.3389/fmars.2019.00422

[17] Sylaios, G., Papadopoulou, A., Tsikliras, A., et al. (2018). ODYSSEA Deliverable 13.2: Data post-processing procedures (Final operational report). Democritus University of Thrace. https://odysseaplatform.eu/download/deliverables/ODYSSEA_Deliverable-13.2.pdf (accessed 10 December 2024).

[18] Kokkos, N., Papadopoulou, A., Zachopoulos, K., Zoidou, M., Beguery, L., Margirier, F., & Sylaios, G., 2023. Hydrography and deep chlorophyll maximum patterns of the Athos Basin and the Thracian Sea continental shelf (North Aegean Sea) combining glider and satellite observations. Continental Shelf Research, 262, 105029. https://doi.org/10.1016/j.csr.2023.105029

[19] Donnelly, J., Abolfathi, S., Pearson, J., Chatrabgoun, O., & Daneshkhah, A., 2022. Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model. Water Research, 225, 119100. https://doi.org/10.1016/j.watres.2022.119100

[20] Bouazzati, H., Damghi, A., El M’rini, A., Maanan, M., & [Author 5], 2025. Evaluating microplastic concentrations in the Al Hoceima Marine Protected Area: Implications for identifying pollution hotspots and formulating conservation strategies. Sustainable Marine Structures, 7(2), April 2025. https://doi.org/10.36956/sms.v7i2.1809

[21] He, S., Jin, S., Chen, J., et al., 2022. Hydrodynamic design and analysis of a hybrid-driven underwater vehicle with an ultra-wide speed range. Ocean Engineering. 264, 112494. DOI: https://doi.org/10.1016/j.oceaneng.2022.112494

[22] Griffiths, G. (ed.), 2002. Technology and Applications of Autonomous Underwater Vehicles. Taylor & Francis: London, UK. DOI: https://doi.org/10.1201/9780203522301

[23] Nezhad, M.M., Neshat, M., Piras, G., et al., 2022. Marine online platforms of services to public end-users—The innovation of the ODYSSEA project. Remote Sensing. 14(3), 572. DOI: https://doi.org/10.3390/rs14030572

[24] Liang, L., Ma, H., Zhao, L., et al., 2024. Vehicle Detection Algorithms for Autonomous Driving: A Review. Sensors. 24(10), 3088. DOI: https://doi.org/10.3390/s24103088

[25] ALSEAMAR, 2022. Survey Missions – Oceans Science. Available from: https://www.alseamar-alcen.com/ocean-science-sector/survey-missions-oceans-science/ (cited 15 June 2024).

[26] Barrett, T., & Mishra, A., 2025. Statistical study of sensor data and investigation of ML-based calibration algorithms for inexpensive sensor modules: Experiments from Cape Point. arXiv preprint, arXiv:2503.13487. https://doi.org/10.48550/arXiv.2503.13487(accessed 15 March 2025).

[27] Wang, B., Sun, Z., Jiang, X., et al., 2023. Kalman Filter and Its Application in Data Assimilation. Atmosphere. 14(8), 1319. DOI: https://doi.org/10.3390/atmos14081319

[28] Lei, X., Zhang, Z., Dong, P., 2018. Dynamic Path Planning of Unknown Environments Based on Deep Reinforcement Learning. Journal of Robotics. 2018(1), 5781591. DOI: https://doi.org/10.1155/2018/5781591

[29] Nejatbakhsh Esfahani, H.; Bahari Kordabad, A.; Moreno‑Salinas, D., 2024. Advanced Control Strategies for Autonomous Maritime Systems, Special Issue Call. Journal of Marine Science and Engineering. Available from: https://www.mdpi.com/journal/jmse/special_issues/9Q0Y5EOJ3B (accessed 15 March 2025).

[30] Ford, D.A., Grossberg, S., Rinaldi, G., et al., 2022. A Solution for Autonomous, Adaptive Monitoring of Coastal Ocean Ecosystems: Integrating Ocean Robots and Operational Forecasts. Frontiers in Marine Science. 9, 1067174. DOI: https://doi.org/10.3389/fmars.2022.1067174

[31] Amavasai, A., Tahershamsi, H., Wood, T., et al., 2024. Data assimilation for Bayesian updating of predicted embankment response using monitoring data. Computers and Geotechnics. 165, 105936. DOI: https://doi.org/10.1016/j.compgeo.2023.105936

[32] Zhou, H., Zeng, Z., Lian, L., 2017. Adaptive re-planning of AUVs for environmental sampling missions: A fuzzy decision support system based on multi-objective particle swarm optimization. International Journal of Fuzzy Systems. 20, 650–671. DOI: https://doi.org/10.1007/s40815-017-0398-7

[33] Bogomolov, V., 2023. AUV positioning by the ranges to less than three acoustic beacons based on recursive Bayesian estimation. Proceedings of the 2023 International Conference on Ocean Studies (ICOS); 03–06 October 2023; Vladivostok, Russia. pp. 037–040. DOI: https://doi.org/10.1109/ICOS60708.2023.10425637

[34] Nag, P., Sun, Y., Reich, B.J., 2023. Spatio-temporal DeepKriging for interpolation and probabilistic forecasting. Spatial Statistics. 57, 100773. DOI: https://doi.org/10.1016/j.spasta.2023.100773

[35] Chen, W., Li, Y., Reich, B.J., et al., 2020. DeepKriging: Spatially dependent deep neural networks for spatial prediction. arXiv preprint arXiv:2007.11972. DOI: https://doi.org/10.48550/arXiv.2007.11972

[36] Cui, T., Pagendam, D.E., Gilfedder, M., 2021. Gaussian process machine learning and Kriging for groundwater salinity interpolation. Environmental Modelling & Software. 144, 105170. DOI: https://doi.org/10.1016/j.envsoft.2021.105170

[37] Marinescu, M., 2024. Explaining and Connecting Kriging with Gaussian Process Regression. arXiv preprint arXiv:2408.02331. DOI: https://doi.org/10.48550/arXiv.2408.02331

[38] Papritz, A., Stein, A., 1999. Spatial Prediction by Linear Kriging. In: Stein, A., van der Meer, F.D., Gorte, B. (eds.). Spatial Statistics for Remote Sensing. Springer: Dordrecht, Netherlands. pp. 83–113. DOI: https://doi.org/10.1007/0-306-47647-9_6

[39] Ponnuru, A., Madhuri, J., Saravanan, S., et al., 2025. Data-driven approaches to water quality monitoring: Leveraging AI, machine learning, and management strategies for environmental protection. Journal of Neonatal Surgery. 14(5S), 664–675. DOI: https://doi.org/10.52783/jns.v14.2107

[40] García Molinos, J., Halpern, B.S., Schoeman, D.S., et al., 2016. Climate velocity and the future global redistribution of marine biodiversity. Nature Climate Change. 6(1), 83–88. DOI: https://doi.org/10.1038/nclimate2769

[41] Barton, A.D., Irwin, A.J., Finkel, Z.V., et al., 2016. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proceedings of the National Academy of Sciences. 113(11), 2964–2969. DOI: https://doi.org/10.1073/pnas.1519080113

[42] Rasmussen, C.E., Williams, C.K.I., 2006. Gaussian Processes for Machine Learning. MIT Press: Cambridge, MA, USA. DOI: https://doi.org/10.7551/mitpress/3206.001.0001

[43] Vargas-Yáñez, M., Plaza, F., García Lafuente, J., et al., 2002. About the seasonal variability of the Alboran Sea circulation. Journal of Marine Systems. 35(3–4), 229–248. DOI: https://doi.org/10.1016/S0924-7963(02)00128-8

[44] Qiu, H., Li, T., Zhang, B., 2024. The impact of climate change on the earth system and its simulation predictions: Progress, challenges, and future directions. Geographical Research Bulletin. 3, 231–246. DOI: https://doi.org/10.50908/grb.3.0_231

[45] Zhang, J., Liu, M., Zhang, S., et al., 2022. Multi-AUV adaptive path planning and cooperative sampling for ocean scalar field estimation. IEEE Transactions on Instrumentation and Measurement. 71, 9505514. DOI: https://doi.org/10.1109/TIM.2022.3167784

[46] Chang, N.-B., Bai, K., 2018. Multisensor data fusion and machine learning for environmental remote sensing. CRC Press: Boca Raton, FL, USA. DOI: https://doi.org/10.1201/b20703

[47] Lacava, T., Bernini, G., Ciancia, E., et al., 2014. Integration of satellite data and in situ measurements for coastal water quality monitoring: Preliminary results of the first IOSMOS (Ionian Sea Water Quality Monitoring by Satellite Data) campaigns. Proceeding of the 2014 EUMETSAT Meteorological Satellite Conference; 22–26 September 2014; Geneva, Switzerland. DOI: https://doi.org/10.13140/2.1.4039.2487

[48] Chen, Z., Fan, J., Wang, K., 2020. Remarks on multivariate Gaussian processes. arXiv preprint arXiv:2010.09830. DOI: https://doi.org/10.48550/arXiv.2010.09830

[49] Zhuang, X., Hu, Y., 2017. Statistical deformation model: Theory and methods. In: Zheng, G., Li, S., Székely, G. (eds.). Statistical Shape and Deformation Analysis: Methods, Implementation, and Applications. Academic Press: Cambridge, MA, USA. pp. 33–65.

[50] Yi, C., Zhang, K., Peng, N., 2019. A multi-sensor fusion and object tracking algorithm for self-driving vehicles. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 233(9), 2293–2300.

[51] Fiorelli, E., Bhatta, P., Leonard, N.E., et al., 2003. Adaptive sampling using feedback control of an autonomous underwater glider fleet. Proceedings of the 13th International Symposium on Unmanned Untethered Submersible Technology (UUST); 1 January 2003; Durham, NH, USA. pp. 1–16.

[52] Clemens, J., Wellhausen, C., Koller, T.L., et al., 2020. Kalman filter with moving reference for jump-free, multi-sensor odometry with application in autonomous driving. Proceedings of the 2020 IEEE 23rd International Conference on Information Fusion (FUSION); 6–9 July 2020; Rustenburg, South Africa. pp. 1–9. DOI: https://doi.org/10.23919/FUSION45008.2020.9190464

[53] Liu, L., Sukhatme, G.S., 2016. Making decisions with spatially and temporally uncertain data. arXiv preprint arXiv:1605.01018v1. DOI: https://doi.org/10.48550/arXiv.1605.01018

[54] Lin, Z., Yin, F., Maroñas, J., 2023. Towards flexibility and interpretability of Gaussian process state-space model. arXiv preprint arXiv:2301.08843. DOI: https://doi.org/10.48550/arXiv.2301.08843

[55] Cressie, N., Wikle, C.K., 2011. Statistics for Spatio-Temporal Data. John Wiley & Sons: Hoboken, NJ, USA. Data. John Wiley & Sons: Hoboken, NJ, USA.