20.500.12556/RUL-163124 Explainable machine learning for predicting diarrhetic shellfish poisoning events in the Adriatic Sea using long-term monitoring data 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 diarrhetic shellfish toxins in mussels (Mytilus galloprovincialis), we train and evaluate the performance of machine learning (ML) models to accurately predict diarrhetic shellfish poisoning (DSP) events. Based on the F1 score, the random forest model provided the best prediction of toxicity results at which the harvesting of mussels is stopped according to EU regulations. Explainability methods such as permutation importance and Shapley Additive Explanations (SHAP) identified key species (Dinophysis fortii and D. caudata) and environmental factors (salinity, river discharge and precipitation) as the best predictors of DSP toxins above regulatory limits. These findings are important for improving early warning systems, which until now were based solely on empirically defined alert abundances of DSP species. They provide experts, aquaculture practitioners, and authorities with additional information to make informed risk management decisions. harmful algal blooms DSP toxins machine learning explainable artificial intelligence aquaculture marine ecology Adriatic Sea true false true Angleški jezik Ni določen Članek v reviji 2024-10-02 10:28:11 2024-10-02 10:28:15 2024-10-07 03:36:46 0000-00-00 00:00:00 2024 0 0 14 str. Vol. 139, art. 102728 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 636.09:616 1878-1470 10.1016/j.hal.2024.102728 209760771 1-s2.0-S1568988324001616-main.pdf 1-s2.0-S1568988324001616-main.pdf 1 F809CBDC9092F1F60C81A369E854ECB0 f748a83338405ee9a41fb7b8ac67e8882354621be8aa641513120137aaede94d 7696254a-8098-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=192615 https://www.sciencedirect.com/science/article/pii/S1568988324001616 0 1cff8e68-8098-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=192612 Veterinarska fakulteta 0 0 0