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Article

Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

1
Remote Sensing and GIS Laboratory, Department of Applied Physics, Sciences Faculty, University of Vigo, Campus Lagoas Marcosende, 36310 Vigo, Spain
2
Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and José Viqueira
ISPRS Int. J. Geo-Inf. 2021, 10(4), 199; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040199
Received: 20 January 2021 / Revised: 16 March 2021 / Accepted: 23 March 2021 / Published: 25 March 2021
(This article belongs to the Special Issue Large Scale Geospatial Data Management, Processing and Mining)
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. View Full-Text
Keywords: harmful algal blooms (HABs); Pseudo-nitzschia spp.; Galician Rias Baixas; coastal embayment; support vector machines (SVMs); neural networks (NNs); Random Forest (RF); AdaBoost harmful algal blooms (HABs); Pseudo-nitzschia spp.; Galician Rias Baixas; coastal embayment; support vector machines (SVMs); neural networks (NNs); Random Forest (RF); AdaBoost
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MDPI and ACS Style

Aláez, F.M.B.; Palenzuela, J.M.T.; Spyrakos, E.; Vilas, L.G. Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain). ISPRS Int. J. Geo-Inf. 2021, 10, 199. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040199

AMA Style

Aláez FMB, Palenzuela JMT, Spyrakos E, Vilas LG. Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain). ISPRS International Journal of Geo-Information. 2021; 10(4):199. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040199

Chicago/Turabian Style

Aláez, Francisco M.B., Jesus M.T. Palenzuela, Evangelos Spyrakos, and Luis G. Vilas 2021. "Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)" ISPRS International Journal of Geo-Information 10, no. 4: 199. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040199

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