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.

翻译：本研究应用可解释机器学习技术，预测由有害藻华引起的的里雅斯特湾（亚得里亚海）贻贝毒性。通过分析新构建的包含28年贻贝养殖区有毒浮游植物记录及贻贝（Mytilus galloprovincialis）毒素浓度数据集，我们训练并评估了机器学习模型预测腹泻性贝毒事件的性能。基于F1分数，随机森林模型对阳性毒性结果的预测效果最优。采用排列重要性与SHAP等可解释性方法，分析表明关键物种（渐尖鳞藻与具尾鳞藻）及环境因子（盐度、河流径流量与降水量）是腹泻性贝毒暴发的最佳预测因子。该发现对完善早期预警系统及支持可持续水产养殖实践具有重要意义。