ItemConference Paper

Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy

Title
Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy
Creator
Biju, Nandana; Ramasamy, Gobi; Syam Mohan, E.
Description
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.
Source
2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Climate Indices; ENSO; Interpretable Deep Learning; Marine Microplastics; Seasonal Decomposition; Spatiotemporal Forecasting; Sustainable Ocean Policy; Theil-Sen Regression
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833158894-6;
Format
online
Language
English
Type
Conference paper

Citation

Biju, Nandana; Ramasamy, Gobi; Syam Mohan, E., “Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25809.

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