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Microplastic Pollution Prediction
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This machine learning project used XGBoost and environmental variables (ocean currents, sea surface temperature, proximity to cities) to predict microplastic hotspots in marine environments. The model achieved high accuracy and could help environmental agencies prioritize cleanup efforts without expensive field sampling.
Microplastic contamination poses serious risks to marine life, ecosystems, and human health. Identifying high-risk areas is challenging due to complex environmental factors. This project proposes a machine learning-based model—using data like ocean currents, sea surface temperature, and proximity to cities—to predict microplastic hotspots. By applying techniques like XGBoost, the model achieves high accuracy and helps environmental agencies target cleanup efforts efficiently. It reduces the need for costly field sampling and showcases how AI can enhance sustainability and environmental monitoring. INDEX TERMS: :Microplastic Pollution, Environmental Monitoring, Machine Learning, XGBoost, Ocean Current Dynamics, Sea Surface Temperature, Predictive Modeling, Aquatic Ecosystems, Supervised Learning, Data Preprocessing, Feature Engineering, Artificial Intelligence (AI), Sustainability, Pollution Hotspots, Geographic Information System (GIS).
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Researchers developed a machine learning framework integrated with geospatial analysis to predict microplastic pollution density across ocean regions. The Gradient Boosting model achieved the highest accuracy with 97% predictive performance, and spatial visualizations revealed pollution hotspots concentrated near industrial coastlines and major ocean current pathways.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.