Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Biju, Nandana; Ramasamy, Gobi; Syam Mohan, E. Title A name given to the resource Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CSITSS67709.2025.11294463" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSITSS67709.2025.11294463</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031435646?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031435646?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biju N., Christ University, Department of Computer Science, Bangalore, India; Ramasamy G., Christ University, Department of Computer Science, Bangalore, India; Syam Mohan E., Christ University, Department of Computer Science, Bangalore, India Description An account of the resource Marine microplastic contamination presents a significant risk to ocean health, necessitating precise spatiotemporal predictions for effective marine policy development. This study introduces a transparent deep learning model to examine and forecast microplastic levels in global oceans by leveraging historical sampling data, seasonal variations, and climatic factors. A comprehensive global dataset is curated and analyzed, integrating environmental indices such as ENSO, PDO, NAO, and MEI to model the influence of large-scale ocean-atmosphere interactions. Temporal decomposition, Mann-Kendall trend testing, Theil-Sen regression, and seasonal analysis reveal statistically significant monthly and interannual variations in microplastic concentration. Correlations with climate drivers underscore the dynamic environmental control on pollutant distribution. By incorporating interpretable environmental modeling, the proposed framework supports data-driven marine pollution mitigation and policy strategies aligned with UN Sustainable Development Goal 14 (Life Below Water). This work establishes a foundation for future extensions involving LSTM- and Transformer-based time series forecasting combined with SHAP-based explainability for enhanced decision-making. Furthermore, anomaly detection employing Prophet residuals and Isolation Forest reveals sudden increases in pollutants, providing early warning systems for disturbances to marine ecosystems. High-risk areas that need focused regulatory actions are further identified using clustering analysis. All things considered, the model makes it possible to forecast marine plastic pollution in a comprehensive, comprehensible, and scalable manner-a crucial component of sustainable ocean governance. 2025 IEEE. Subject The topic of the resource Climate Indices; ENSO; Interpretable Deep Learning; Marine Microplastics; Seasonal Decomposition; Spatiotemporal Forecasting; Sustainable Ocean Policy; Theil-Sen Regression Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158894-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online