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

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. bloom...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2021-04, Vol.10 (4), p.199
Main Authors: Aláez, Francisco M. Bellas, Palenzuela, Jesus M. Torres, Spyrakos, Evangelos, Vilas, Luis González
Format: Article
Language:English
Subjects:
Accuracy
Algae
Algorithms
Blooms
coastal embayment
Coasts
Datasets
Decision trees
Drowned valleys
False alarms
Galician Rias Baixas
harmful algal blooms (HABs)
Learning algorithms
Machine learning
Methods
Neural networks
neural networks (NNs)
Performance measurement
Prediction models
Pseudo-nitzschia
Pseudo-nitzschia spp
Sensitivity
Specificity
Support vector machines
support vector machines (SVMs)
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi10040199