Explainable machine learning for predicting shellfish toxicity in the Adriatic Sea using long-term monitoring data of HABs

• URL: http://arxiv.org/abs/2405.04372v2
• Date: Thu, 9 May 2024 09:46:35 GMT
• Title: Explainable machine learning for predicting shellfish toxicity in the Adriatic Sea using long-term monitoring data of HABs
• Authors: Martin Marzidovšek, Janja Francé, Vid Podpečan, Stanka Vadnjal, Jožica Dolenc, Patricija Mozetič,
• Abstract summary: We train and evaluate machine learning models to accurately predict diarrhetic shellfish poisoning events. The random forest model provided the best prediction of positive toxicity results based on the F1 score. Key species (Dinophysis fortii and D. caudata) and environmental factors (salinity, river discharge and precipitation) were the best predictors of DSP outbreaks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by harmful algal blooms. By analysing a newly created 28-year dataset containing records of toxic phytoplankton in mussel farming areas and toxin concentrations in mussels (Mytilus galloprovincialis), we train and evaluate the performance of ML models to accurately predict diarrhetic shellfish poisoning (DSP) events. The random forest model provided the best prediction of positive toxicity results based on the F1 score. Explainability methods such as permutation importance and SHAP identified key species (Dinophysis fortii and D. caudata) and environmental factors (salinity, river discharge and precipitation) as the best predictors of DSP outbreaks. These findings are important for improving early warning systems and supporting sustainable aquaculture practices.

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