XAI for Decision Support in Microplastic Pollution Management

The Microplastic pollution management is the instructive and influential environmental issue that needs sophisticated decision support systems for efficient and significant management of microplastic pollution and very risks and poses for serious and decisive with and to the human health and aquatic ecosystems. The management systems are essential for the human beings and additionally automated solutions are frequently viewed with suspicion due to the intricacy of the ecosystems and the opaqueness of AI models. By improving the ability to be explained and detailed of AI-driven decision support systems, XAI Artificial Intelligence offers a remedy by empowering environmental scientists and policymakers to comprehend and verify model predictions. With an emphasis on how interpretable machine learning approaches might enhance monitoring, risk assessment and risk management and security for the microplastic pollution frameworks, and remediation strategies and integrated with AI frameworks, this study examines the use of XAI with integrated AI support in managing microplastic pollution. We go over several XAI techniques to improve model transparency, including and using Shapley and Cognitive computing and Generative transform XAI and Additive Explanations with Local Interpretable Paradigm-agnostic explanations. We also provide case studies that demonstrate how XAI is being used in real-world environmental sustainability applications. Explainability and comprehensibility can be included into machine learning-driven decision support to foster trust, assist well-informed policymakers, and create more potent mitigation plans for microplastic contamination and potential mitigation and support of XAI AI and collected data by Remote Sensing and XAI with Data collections and analytics.