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Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy
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Researchers developed an interpretable deep learning model integrating historical microplastic sampling data, seasonal patterns, and large-scale ocean-atmosphere climate indices to forecast spatiotemporal marine microplastic distribution, identifying climate drivers and offering a policy-relevant tool for ocean pollution management.
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.
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Decoding the PlasticPatch: Exploring the Global MicroplasticDistribution in the Surface Layers of Marine Regions with InterpretableMachine Learning
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Researchers applied four interpretable machine learning algorithms to a calibrated global marine microplastic dataset to construct a predictive model of surface-layer microplastic distribution, finding that biogeochemical and anthropogenic factors are the dominant drivers of global marine microplastic pollution patterns.
Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning
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Researchers used interpretable machine learning algorithms to predict global marine microplastic distribution patterns based on calibrated field data. The study found that biogeochemical and human activity factors had the greatest influence on microplastic concentrations, which ranged from about 0.2 to 27 particles per cubic meter across the world's oceans, providing a framework for pollution management and decision-making.
Interpretable deep-learning assessment of environmental indicators of global marine plastic pollution across ocean basins and climate zones
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Scientists used artificial intelligence to predict where ocean plastic pollution is likely to accumulate, based on factors like water oxygen levels, distance from coastlines, and the amount of tiny plant life (phytoplankton) in the water. The key finding: what drives plastic pollution differs significantly by region, meaning a one-size-fits-all cleanup or prevention strategy won't work as well as targeted, location-specific approaches. This matters because better prediction of plastic hotspots could help protect seafood supplies and coastal communities from the microplastics that eventually make their way into the food we eat.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
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Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
Monitoring Marine Microplastic Distributions in Data-Scarce Ocean Regions Using Environmental Representation Learning and Explainable Machine Learning
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Researchers built a computer model that combines ocean pollution records with data on water depth, temperature, and currents to predict where microplastic pollution collects in the ocean, including areas with little existing data. The model pointed to coastal seas like the Bay of Bengal, South China Sea, Gulf of Mexico and Mediterranean Sea as likely pollution hotspots, though it worked less well when applied to new, different regions.
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