{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T03:01:37Z","timestamp":1767841297676,"version":"3.49.0"},"reference-count":98,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme (grant agreement n\u00b0 776348).","award":["776348"],"award-info":[{"award-number":["776348"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org\/10.1016\/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.<\/jats:p>","DOI":"10.3390\/ijgi10040199","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T14:51:12Z","timestamp":1616683872000},"page":"199","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1648-5984","authenticated-orcid":false,"given":"Francisco M. Bellas","family":"Al\u00e1ez","sequence":"first","affiliation":[{"name":"Remote Sensing and GIS Laboratory, Department of Applied Physics, Sciences Faculty, University of Vigo, Campus Lagoas Marcosende, 36310 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesus M. Torres","family":"Palenzuela","sequence":"additional","affiliation":[{"name":"Remote Sensing and GIS Laboratory, Department of Applied Physics, Sciences Faculty, University of Vigo, Campus Lagoas Marcosende, 36310 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7970-5211","authenticated-orcid":false,"given":"Evangelos","family":"Spyrakos","sequence":"additional","affiliation":[{"name":"Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0854-8284","authenticated-orcid":false,"given":"Luis Gonz\u00e1lez","family":"Vilas","sequence":"additional","affiliation":[{"name":"Remote Sensing and GIS Laboratory, Department of Applied Physics, Sciences Faculty, University of Vigo, Campus Lagoas Marcosende, 36310 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4975","DOI":"10.1073\/pnas.1619575114","article-title":"Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacific oceans","volume":"114","author":"Gobler","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101590","DOI":"10.1016\/j.hal.2019.03.008","article-title":"Harmful algal blooms: A climate change co-stressor in marine and freshwater ecosystems","volume":"91","author":"Griffith","year":"2020","journal-title":"Harmful Algae"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1146\/annurev-marine-120308-081121","article-title":"Progress in understanding harmful algal blooms: Paradigm shifts and new technologies for research, monitoring, and management","volume":"4","author":"Anderson","year":"2012","journal-title":"Ann. Rev. Mar. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.ocecoaman.2009.04.006","article-title":"Approaches to monitoring, control and management of harmful algal blooms (HABs)","volume":"52","author":"Anderson","year":"2009","journal-title":"Ocean Coast Manag."},{"key":"ref_5","unstructured":"Shroeder, J.F., Ellis, J.T., and Sherman, D.J. (2015). Living with harmful algal blooms in a changing world: Strategies for modeling and mitigating their effects in coastal marine ecosystems. Coastal and Marine Hazards, Risks, and Disasters, Elsevier."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.jmarsys.2010.04.003","article-title":"Predicting potentially toxigenic Pseudo-nitzschia blooms in the Chesapeake Bay","volume":"83","author":"Anderson","year":"2010","journal-title":"J. Mar. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.jglr.2019.03.004","article-title":"Extending the forecast model: Predicting Western Lake Erie harmful algal blooms at multiple spatial scales","volume":"45","author":"Manning","year":"2019","journal-title":"J. Great Lakes Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"37","DOI":"10.3354\/meps07999","article-title":"Development of a logistic regression model for the prediction of toxigenic Pseudo-nitzschia blooms in Monterey Bay, California","volume":"383","author":"Lane","year":"2009","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.jmarsys.2010.05.001","article-title":"A simple short range model for the prediction of harmful algal events in the bays of southwestern Ireland","volume":"83","author":"Raine","year":"2010","journal-title":"J. Mar. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1016\/j.ecolmodel.2011.02.013","article-title":"Descriptive and prediction models of phytoplankton in the northern Adriatic","volume":"222","author":"Volf","year":"2011","journal-title":"Ecol. Model."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1002\/ecy.1804","article-title":"Predicting coastal algal blooms in southern California","volume":"98","author":"McGowan","year":"2017","journal-title":"Ecology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101906","DOI":"10.1016\/j.hal.2020.101906","article-title":"Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva","volume":"99","author":"Derot","year":"2020","journal-title":"Harmful Algae"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Humphries, G., Magness, D.R., and Huettmann, F. (2018). Use of machine learning (ML) for predicting and analyzing ecological and \u2018presence only\u2019 data: An overview of applications and a good outlook. Machine Learning for Ecology and Sustainable Natural Resource Management, Springer International Publishing.","DOI":"10.1007\/978-3-319-96978-7"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101729","DOI":"10.1016\/j.hal.2019.101729","article-title":"Modeling harmful algal blooms in a changing climate","volume":"91","author":"Ralston","year":"2020","journal-title":"Harmful Algae"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"115959","DOI":"10.1016\/j.watres.2020.115959","article-title":"A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes","volume":"182","author":"Rousso","year":"2020","journal-title":"Water Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107334","DOI":"10.1016\/j.ecolind.2020.107334","article-title":"Predicting coastal algal blooms with environmental factors by machine learning methods","volume":"123","author":"Yu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 27\u201329). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Suykens, J.A.K., and Vandewalle, J. (1998). The Support Vector method of function estimation. Nonlinear Modeling, Springer.","DOI":"10.1007\/978-1-4615-5703-6"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (2000). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.pocean.2014.03.003","article-title":"Support Vector Machine-based method for predicting Pseudo-nitzschia spp. blooms in coastal waters (Galician rias, NW Spain)","volume":"124","author":"Spyrakos","year":"2014","journal-title":"Prog. Oceanogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"397473","DOI":"10.1155\/2012\/397473","article-title":"Freshwater Algal Bloom Prediction by Support Vector Machine in Macau Storage Reservoirs","volume":"2012","author":"Chen","year":"2012","journal-title":"Math. Probl. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.amc.2015.03.075","article-title":"A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: A case study in Northern Spain","volume":"260","year":"2015","journal-title":"Appl. Math. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.ecolmodel.2007.10.018","article-title":"A comparative study on predicting algae blooms in Douro River, Portugal","volume":"212","author":"Ribeiro","year":"2008","journal-title":"Ecol. Model."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5907","DOI":"10.1016\/j.apm.2015.04.001","article-title":"Integrating support vector regression with particle swarm optimization for numerical modeling for algal blooms of freshwater","volume":"39","author":"Lou","year":"2015","journal-title":"Appl. Math. Model."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ecolmodel.2019.02.005","article-title":"A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading","volume":"398","author":"Shen","year":"2019","journal-title":"Ecol. Model."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ecoinf.2017.09.004","article-title":"Consensus methods based on machine learning techniques for marine phytoplankton presence\u2013absence prediction","volume":"42","author":"Bourel","year":"2017","journal-title":"Ecol. Inf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/JSTARS.2010.2103927","article-title":"A machine learning based spatio-temporal data mining approach for detection of harmful algal blooms in the Gulf of Mexico","volume":"4","author":"Gokaraju","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3229","DOI":"10.1109\/JSTARS.2020.3001445","article-title":"HABNet: Machine Learning, Remote Sensing-Based Detection of Harmful Algal Blooms","volume":"13","author":"Hill","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, X., Yu, J., Jia, Z., and Song, J. (2014, January 3\u20135). Harmful algal blooms prediction with machine learning models in Tolo Harbour. Proceedings of the International Conference on Smart Computing, Hong Kong, China.","DOI":"10.1109\/SMARTCOMP.2014.7043865"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/0304-3800(95)00142-5","article-title":"Application of neural network for nonlinear modeling in ecology","volume":"90","author":"Lek","year":"1996","journal-title":"Ecol. Model."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0092-8240(05)80006-0","article-title":"A Logical Calculus of the Ideas Immanent in Nervous Activity","volume":"52","author":"McCulloch","year":"1990","journal-title":"Bull. Math. Biol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"125","DOI":"10.2166\/hydro.2002.0013","article-title":"Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling of algal blooms in freshwater lakes","volume":"4","author":"Recknagel","year":"2002","journal-title":"J. Hydroinform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1016\/S0043-1354(00)00464-4","article-title":"Use of artificial neural network in the prediction of algal blooms","volume":"35","author":"Wei","year":"2001","journal-title":"Water Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.watres.2016.10.076","article-title":"A novel single-parameter approach for forecasting algal blooms","volume":"108","author":"Xiao","year":"2017","journal-title":"Water Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.jmarsys.2012.12.007","article-title":"Ecological forecasting in Chesapeake Bay: Using a mechanistic\u2013empirical modeling approach","volume":"125","author":"Brown","year":"2013","journal-title":"J. Mar. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ecolmodel.2016.07.009","article-title":"Artificial neural network approach to population dynamics of harmful algal blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo-nitzschia","volume":"338","author":"Guallar","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0304-3800(02)00281-8","article-title":"Neural network modelling of coastal algal blooms","volume":"159","author":"Lee","year":"2003","journal-title":"Ecol. Model."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.hal.2006.11.002","article-title":"Artificial neural network approaches to one-step weekly prediction of Dinophysis acuminata blooms in Huelva (Western Andalucia, Spain)","volume":"6","year":"2007","journal-title":"Harmful Algae"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.envsoft.2014.07.011","article-title":"Proactive management of estuarine algal blooms using an automated monitoring buoy coupled with an artificial neural network","volume":"61","author":"Coad","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.ecolmodel.2017.09.013","article-title":"An optimization of artificial neural network model for predicting chlorophyll dynamics","volume":"364","author":"Tian","year":"2017","journal-title":"Ecol. Model."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Wu, H. (2017, January 24\u201326). Water bloom warning model based on random forest. Proceedings of the 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.","DOI":"10.1109\/ICIIBMS.2017.8279712"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Drew, C., Wiersma, Y., and Huettmann, F. (2011). Modeling Species Distribution and Change Using Random Forest. Predictive Species and Habitat Modeling in Landscape Ecology, Springer.","DOI":"10.1007\/978-1-4419-7390-0"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wei, C.L., Rowe, G.T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M.J., Soliman, Y., Huettmann, F., Qu, F., and Yu, Z. (2010). Global Patterns and Predictions of Seafloor Biomass Using Random Forests. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0015323"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"101174","DOI":"10.1016\/j.ecoinf.2020.101174","article-title":"Benefits of machine learning and sampling frequency on phytoplankton bloom forecasts in coastal areas","volume":"60","author":"Derot","year":"2020","journal-title":"Ecol. Inf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"101918","DOI":"10.1016\/j.hal.2020.101918","article-title":"Random forest classification to determine environmental drivers and forecast paralytic shellfish toxins in Southeast Alaska with high temporal resolution","volume":"99","author":"Harley","year":"2020","journal-title":"Harmful Algae"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4166","DOI":"10.1038\/s41598-019-40664-w","article-title":"A model predicting the PSP toxic dinoflagellate Alexandrium minutum occurrence in the coastal waters of the NW Adriatic Sea","volume":"9","author":"Valbi","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"136173","DOI":"10.1016\/j.scitotenv.2019.136173","article-title":"Predicting fish kills and toxic blooms in an intensive mariculture site in the Philippines using a machine learning model","volume":"707","author":"Ottong","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kadavi, P.R., Lee, C.-W., and Lee, S. (2018). Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sens., 10.","DOI":"10.3390\/rs10081252"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1080\/01431161.2019.1648907","article-title":"Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods","volume":"41","author":"Peng","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1007\/s13042-018-0846-1","article-title":"Predicting algal appearance on mortar surface with ensembles of adaptive neuro fuzzy models: A comparative study of ensemble strategies","volume":"10","author":"Tran","year":"2019","journal-title":"Int. J. Mach. Learn. Cyber."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jmarsys.2008.05.016","article-title":"Skill assessment for an operational algal bloom forecast system","volume":"76","author":"Stumpf","year":"2009","journal-title":"J. Mar. Syst."},{"key":"ref_54","first-page":"25","article-title":"Harmful Algal Blooms: A global overview","volume":"Volume 33","author":"Hallegraeff","year":"2004","journal-title":"Manual on Harmful Marine Microalgae"},{"key":"ref_55","unstructured":"Anderson, D.M., Cembella, E.D., and Hallegraeff, G.M. (1998). Bloom dynamics and physiology of domoic-acid-producing Pseudo-nitzschia species. The Physiological Ecology of Harmful Algal Blooms, Springer."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3354\/meps327119","article-title":"Circulation and environmental conditions during a toxigenic Pseudo-nitzschia australis bloom in the Santa Barbara Channel, California","volume":"327","author":"Anderson","year":"2006","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_57","unstructured":"Reguera, B., Blanco, B., Fern\u00e1ndez, M.L., and Wyatt, T. (1998). Pseudo-nitzschia species isolated from Galician waters: Toxicity, DNA content and lectin binding assay. Harmful Algae, Xunta de Galicia and Intergovernmental Commission of UNESCO."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.hal.2009.11.006","article-title":"Can Pseudo-nitzschia blooms be modeled by coastal upwelling in Lisbon Bay?","volume":"9","author":"Palma","year":"2010","journal-title":"Harmful Algae"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.hal.2018.04.008","article-title":"Morphology and toxicity of Pseudo-nitzschia species in the northern Benguela Upwelling System","volume":"75","author":"Louw","year":"2018","journal-title":"Harmful Algae"},{"key":"ref_60","first-page":"891","article-title":"Development of statistical models for prediction of the neurotoxin domoic acid levels in the pennate diatom Pseudo-nitzschia pungens f. multiseries utilizing data from cultures and natural blooms","volume":"Volume 2","year":"2006","journal-title":"Algal Cultures, Analogues of Blooms and Applications"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.hal.2008.10.005","article-title":"Empirical models of toxigenic Pseudo-nitzschia blooms: Potential use as a remote detection tool in the Santa Barbara Channel","volume":"8","author":"Anderson","year":"2009","journal-title":"Harmful Algae"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.hal.2013.01.004","article-title":"Factors controlling the production of domoic acid by Pseudo-nitzschia (Bacillariophyceae): A model study","volume":"24","author":"Terseleer","year":"2013","journal-title":"Harmful Algae"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S0094-5765(03)00135-8","article-title":"Forecast of red tides off the Galician coast","volume":"53","year":"2003","journal-title":"Acta Astronaut."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.seares.2015.06.012","article-title":"Modelling Pseudo-nitzschia events off southwest Ireland","volume":"105","author":"Cusack","year":"2015","journal-title":"J. Sea Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.hal.2015.11.013","article-title":"Harmful algal bloom forecast system for SW Ireland. Part II: Are operational oceanographic models useful in a HAB warning system","volume":"53","author":"Cusack","year":"2016","journal-title":"Harmful Algae"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1002\/2013JC009622","article-title":"Hindcasts of potential harmful algal bloom transport pathways on the Pacific Northwest coast","volume":"119","author":"Giddings","year":"2014","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1093\/icesjms\/fsy113","article-title":"Harmful algal blooms and climate change: Exploring future distribution changes","volume":"75","author":"Townhill","year":"2018","journal-title":"Ices J. Mar. Sci."},{"key":"ref_68","first-page":"131","article-title":"The seasonal upwelling cycle along the Eastern boundary of the North Atlantic","volume":"34","author":"Wooster","year":"1976","journal-title":"J. Mar. Res."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Richardson, F.A. (1981). Upwelling off the Galician coast, northwest Spain. Coastal Upwelling, American Geophysical Union.","DOI":"10.1029\/CO001"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1357\/002224087788401115","article-title":"The relationship of upwelling to mussel production in the r\u00edas of western coast of Spain","volume":"45","author":"Blanton","year":"1987","journal-title":"J. Mar. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S0025315400039928","article-title":"Preliminary Studies on the Export of Organic Matter During Phytoplankton Blooms off La Coru\u00f1a (Northwestern Spain)","volume":"78","author":"Bode","year":"1998","journal-title":"J. Mar. Biol. Assoc. UK"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ocecoaman.2018.10.012","article-title":"The Galician mussel industry: Innovation and changes in the last forty years","volume":"167","author":"Labarta","year":"2019","journal-title":"Ocean Coast. Manag."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1111\/raq.12465","article-title":"The decline of mussel aquaculture in the European Union: Causes, economic impacts and opportunities","volume":"13","author":"Avdelas","year":"2021","journal-title":"Rev. Aquacult."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1016\/j.rse.2011.05.008","article-title":"Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll an algorithm for MERIS full resolution data during an upwelling cycle","volume":"115","author":"Spyrakos","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_75","first-page":"113","article-title":"Estructura y din\u00e1mica de la \u201cpurga de mar\u201d en R\u00eda de Vigo","volume":"5","author":"Margalef","year":"1956","journal-title":"Investig. Pesq."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3354\/meps112241","article-title":"Upwelling-Downwelling Sequences in the Generation of Red Tides in a Coastal Upwelling System","volume":"112","author":"Tilstone","year":"1994","journal-title":"Mar Ecol. Progr. Ser."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1093\/plankt\/16.7.857","article-title":"Red Tide Assemblage Formation in an Estuarine Upwelling Ecosystem\u2014Ria-De-Vigo","volume":"16","author":"Figueiras","year":"1994","journal-title":"J. Plankton Res."},{"key":"ref_78","unstructured":"Pitcher, P., Moita, T., Trainer, V.L., Kudela, R., Figueiras, P., and Probyn, T. (2005). Global Ecology and Oceanography of Harmful Algal Blooms. GEOHAB Core Research Project: HABs in Upwelling Systems, SCOR."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1093\/plankt\/13.3.589","article-title":"Hydrography and phytoplankton of the R\u00eda de Vigo before and during a red tide of Gymnodinium catenatum Graham","volume":"13","author":"Figueiras","year":"1991","journal-title":"J. Plankton Res."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.hal.2008.04.007","article-title":"Renewal time and the impact of harmful algal blooms on the extensive mussel raft culture of the Iberian coastal upwelling system (SW Europe)","volume":"7","author":"Labarta","year":"2008","journal-title":"Harmful Algae"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.marpol.2010.08.008","article-title":"Are red tides affecting economically the commercialization of the Galician (NW Spain) mussel farming?","volume":"35","author":"Villasante","year":"2011","journal-title":"Mar. Policy"},{"key":"ref_82","first-page":"1","article-title":"Zur vervollkommnung der quantitativen phytoplankton-methodik","volume":"9","year":"1958","journal-title":"Mitt. Int. Ver. Theor. Unde Amgewandte Limnol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1016\/j.csr.2005.06.001","article-title":"Statial analysis of the wind field on the western coast of Galicia (NW Spain) from in situ measurements","volume":"25","author":"Herrera","year":"2005","journal-title":"Cont. Shelf Res."},{"key":"ref_84","unstructured":"Bakun, A. (1973). Coastal Upwelling Indexes, West Coast of North America, 1946\u20131971."},{"key":"ref_85","unstructured":"Sarle, W.S. (1994, January 10\u201313). Neural networks and statistical models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, Cary, NC, USA."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1023\/A:1017181826899","article-title":"Glossary of terms. Machine Learning\u2014Special Issue on Applications of Machine Learning and the Knowledge Discovery Process","volume":"30","author":"Kohavi","year":"1998","journal-title":"Mach. Learn."},{"key":"ref_87","unstructured":"Cohen, W.W., and Moore, A. (2006, January 25\u201329). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning (ICML\u201906), Pittsburgh, PA, USA."},{"key":"ref_88","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2017). The Elements of Statistical Learning. Data Mining, Inference and Prediction, Springer. [2nd ed.]."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Jeni, L.A., Cohn, J.F., and De La Torre, F. (2013, January 2\u20135). Facing Imbalanced Data\u2014Recommendations for the Use of Performance Metrics. Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland.","DOI":"10.1109\/ACII.2013.47"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Kubat, M. (2018). Introduction to Machine Learning, Springer. [2nd ed.].","DOI":"10.1007\/978-3-319-63913-0"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/08839510500313653","article-title":"Evaluation of classifiers for an uneven class distribution problem","volume":"20","author":"Daskalaki","year":"2006","journal-title":"Appl. Artif. Intell."},{"key":"ref_92","unstructured":"Ghoneim, S. (2021, January 18). Accuracy, Recall, Precision, F-Score & Specificity. Which to optimize on? Towards Data Science. Available online: https:\/\/towardsdatascience.com\/accuracy-recall-precision-f-score-specificity-which-to-optimize-on-867d3f11124."},{"key":"ref_93","first-page":"79","article-title":"A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions","volume":"52","author":"Kaur","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.ins.2013.07.007","article-title":"An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics","volume":"250","author":"Fernandez","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.hal.2017.07.005","article-title":"Nutrient ratios influence variability in Pseudo-nitzschia species diversity and particulate domoic acid production in the Bay of Seine (France)","volume":"68","author":"Thorel","year":"2017","journal-title":"Harmful Algae"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Torres Palenzuela, J.M., Gonz\u00e1lez Vilas, L., Bellas, F.M., Garet, E., Gonz\u00e1lez-Fern\u00e1ndez, \u00c1., and Spyrakos, E. (2019). Pseudo-nitzschia Blooms in a Coastal Upwelling System: Remote Sensing Detection, Toxicity and Environmental Variables. Water, 11.","DOI":"10.3390\/w11091954"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.seares.2015.12.006","article-title":"Temporal variation and trends of inorganic nutrients in the coastal upwelling of the NW Spain (Atlantic Galician r\u00edas)","volume":"108","author":"Doval","year":"2016","journal-title":"J. Sea Res."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s41208-019-00180-0","article-title":"Potential Application of the New Sentinel Satellites for Monitoring of Harmful Algal Blooms in the Galician Aquaculture","volume":"36","author":"Pazos","year":"2020","journal-title":"Thalassas"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/4\/199\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:41:09Z","timestamp":1760161269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/4\/199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":98,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["ijgi10040199"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10040199","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]}}}