Hybrid Machine Learning techniques in the management of harmful algal
  blooms impact
Andres Molares-Ulloa Daniel Rivero Jesus Gil Ruiz Enrique Fernandez-Blanco Luis de-la-Fuente-Valentín
Harmful algal blooms (HABs) are episodes of high concentrations of algae that
are potentially toxic for human consumption. Mollusc farming can be affected by
HABs because, as filter feeders, they can accumulate high concentrations of
marine biotoxins in their tissues. To avoid the risk to human consumption,
harvesting is prohibited when toxicity is detected. At present, the closure of
production areas is based on expert knowledge and the existence of a predictive
model would help when conditions are complex and sampling is not possible.
Although the concentration of toxin in meat is the method most commonly used by
experts in the control of shellfish production areas, it is rarely used as a
target by automatic prediction models. This is largely due to the irregularity
of the data due to the established sampling programs. As an alternative, the
activity status of production areas has been proposed as a target variable
based on whether mollusc meat has a toxicity level below or above the legal
limit. This new option is the most similar to the actual functioning of the
control of shellfish production areas. For this purpose, we have made a
comparison between hybrid machine learning models like Neural-Network-Adding
Bootstrap (BAGNET) and Discriminative Nearest Neighbor Classification (SVM-KNN)
when estimating the state of production areas. The study has been carried out
in several estuaries with different levels of complexity in the episodes of
algal blooms to demonstrate the generalization capacity of the models in bloom
detection. As a result, we could observe that, with an average recall value of
93.41% and without dropping below 90% in any of the estuaries, BAGNET
outperforms the other models both in terms of results and robustness.