Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28742
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Support Vector Machine-based method for predicting Pseudo-nitzschia spp. blooms in coastal waters (Galician rias, NW Spain)
Author(s): González Vilas, Luis
Spyrakos, Evangelos
Torres Palenzuela, Jesus M
Pazos, Yolanda
Contact Email: evangelos.spyrakos@stir.ac.uk
Issue Date: 31-May-2014
Citation: González Vilas L, Spyrakos E, Torres Palenzuela JM & Pazos Y (2014) Support Vector Machine-based method for predicting Pseudo-nitzschia spp. blooms in coastal waters (Galician rias, NW Spain). Progress in Oceanography, 124, pp. 66-77. https://doi.org/10.1016/j.pocean.2014.03.003.
Abstract: Phytoplanktonic blooms in the coastal embayments (rias) at the NW part of Spain were mentioned for the first time in 1918 and since then they have been associated numerous times with negatives impacts to a very important economic activity in the area, mussel production. In this study, eight years of Pseudo-nitzschia spp. abundance and associated meteorological and oceanographic data were used to develop and validate support vector machine (SVM) models for the prediction of these diatoms. SVM were used to identify presence/below low detection limit, bloom/no bloom conditions of Pseudo-nitzschia spp. and finally to predict blooms due to these diatoms in the coastal systems of the Galician rias. The best SVM models were selected on the basis of C and γ parameters and their performance was evaluated in terms of accuracy and kappa statistics (κ). Regarding the presence/below low detection limit, bloom/no bloom models the best results in the validation dataset were achieved using all the variables: ria code, day of the year, temperature, salinity, upwelling indices and bloom occurrence in previous weeks. The best performing models were also tested in an independent dataset from the study area, where they showed high overall accuracy (78.53-82.18%), κ values (0.77-0.81) and true positive rates (62.60-78.18). In these models the bloom occurrence in previous weeks was identified as a key parameter to the prediction performance. In this paper, toxic Pseudo-nitzschia blooms could not be predicted due to limited information on toxin concentration and species composition. Nevertheless, this study demonstrates that the approach followed here is capable for high predictive performance which could be of great aid in the monitoring of algal blooms and offer valuable information to the local shellfish industry. The reliable prediction of categorical Pseudo-nitzschia abundances using variables that are operationally determined or short-term predicted could provide early warning of an impending bloom and could help to the development of strategies that could minimize the risks to human health and protect valuable economic resources.
DOI Link: 10.1016/j.pocean.2014.03.003
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