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

Abstract

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.

Variability patterns and phenology of harmful phytoplankton blooms off southern Portugal: Looking for region-specific environmental drivers and predictors

“…Over these regions and, also considering information available for adjacent Spanish (northwest) coastal waters, blooms of Pseudo-nitzschia H. Peragallo, 1900, related with the amnesic shellfish poisoning (ASP) syndrome in humans, are recurrent events during the spring-summer upwelling period (Palma et al, 2010;Vidal et al, 2017;Palenzuela et al, 2019). Blooms of Dinophysis Ehrenberg, 1839, associated with diarrhetic shellfish poisoning (DSP) syndrome, are also recurrent off northwestern Iberia during the upwelling season, but favored in periods of thermohaline stratification (Reguera et al, 1995;Moita et al, 2006aMoita et al, , 2016Escalera et al, 2010;Velo-Suárez et al, 2014;Díaz et al, 2016Díaz et al, , 2019Vidal et al, 2017;Aláez et al, 2021). Blooms of Gymnodinium catenatum H.W.…”