RT Journal Article
T1 Machine learning methods applied to the prediction of pseudo-nitzschia spp. blooms in the Galician Rias Baixas (NW Spain)
A1 Bellas Aláez, Francisco Miguel
A1 Torres Palenzuela, Jesus Manuel
A1 Spyrakos, Evangelos
A1 González Vilas, Luís
K1 2510.01 Oceanografía Biológica
K1 1209.14 Técnicas de Predicción Estadística
K1 2417.05 Biología Marina
AB 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.
PB ISPRS International Journal of Geo-Information
SN 22209964
YR 2021
FD 2021-03-25
LK http://hdl.handle.net/11093/2096
UL http://hdl.handle.net/11093/2096
LA eng
NO ISPRS International Journal of Geo-Information, 10(4): 199 (2021)
NO European Commission | Ref. H2020, n. 776348
DS Investigo
RD 08-sep-2024