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XAI for Decision Support in Microplastic Pollution Management
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Researchers trained machine learning models on a dataset of microplastic fibers, beads, and fragments, finding that Random Forest achieved the best classification accuracy with particle size and color intensity as the most predictive features, and that integrating SHAP-based explainability reduced expert decision time by over 60% compared to opaque AI outputs.
The continuing increase of microplastic pollution requires urgent development of proper ecological monitoring alongside classification systems. The paper investigates how Explainable Artificial Intelligence (XAI) frameworks react with microplastic pollution management tools to enhance decision processes while increasing transparency capabilities. The training of machine learning models through a dataset consisting of fiber beads and fragments yielded Random Forest as the optimal selection because it provided exceptional accuracy together with ease of interpretation. The key characteristics for prediction turned out to be size, alongside color intensity according to both feature importance and SHAP analysis results. The confusion matrix showed both precise classifications and very low rates of incorrect categorizations. XAI implementation delivered both extraordinary performance outcomes with expert trust enhancement and significantly short time-to-decision of more than sixty percent according to Likert-scale survey results. The examination outcomes establish XAI works to close the distance between machine-generated technical AI outputs and field-oriented implementation decisions. The system provides plain understandable explanations that enable faster and more self-assured action from researchers and policymakers. The study presents an implementation framework for explainable modeling in environmental oversight which serves as a foundation for developing sustainable AI applications in sustainability science.
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XAI for Decision Support in Microplastic Pollution Management
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Researchers examined how explainable AI (XAI) techniques—including SHAP values, LIME, and generative models—can make machine learning decision-support systems for microplastic pollution management more transparent, helping environmental scientists and policymakers trust and interpret model-driven risk assessments and remediation strategies.
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Researchers outlined how deep learning and explainable AI are replacing slow, labor-intensive manual methods for microplastic identification, enabling real-time, large-scale detection while addressing key concerns about model transparency, regulatory implications, and the scalability of AI-driven environmental monitoring.
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Random Forest-Based Prediction of Coastal Microplastic Concentration Using High-Dimensional Environmental Data: A Comparative Study with Deep Learning and Machine Learning
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Scientists tested whether AI could predict microplastic pollution levels on coastlines using environmental data, instead of relying on slow, expensive lab testing. A simpler, well-established computer model (Random Forest) turned out to be much more accurate than a fancier "deep learning" AI system at this task. This matters because faster, cheaper ways to track microplastic pollution could help communities identify contaminated areas sooner—which is important since microplastics are increasingly linked to health concerns as they make their way into seafood and drinking water.
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Researchers compared four machine learning models—logistic regression, random forest, support vector machine, and a neural network—for predicting microplastic density in US coastal waters across three regions. The support vector machine performed best with 93.94% average accuracy, demonstrating the potential of AI-driven tools for microplastic monitoring.
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