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Predicting microplastic quantities in Indonesian provincial rivers using machine learning models
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This study used machine learning models to predict microplastic levels in rivers across 24 Indonesian provinces based on environmental and economic data. Temperature, economic output, and population density were the strongest predictors of microplastic pollution. The approach could help environmental agencies monitor and manage microplastic contamination in freshwater systems more efficiently.
Microplastic pollution has surfaced as a critical environmental concern, affecting ecosystems and human health globally. This study explored the application of several machine learning models, including the Tree algorithm, k-Nearest Neighbors (kNN), Random Forest (RF), Linear Regression (LR), Support Vector Machine (SVM), and Neural Networks (NN), to predict microplastic concentrations in the rivers of Indonesia's 24 provinces. By utilizing both environmental and anthropogenic data, the Tree algorithm exhibited the best performance, achieving a coefficient of determination (R) of 0.838 and a mean absolute percentage error (MAPE) of 0.242 on unseen testing data, thereby highlighting strong predictive capability. Key variables influencing microplastic abundance included annual average temperature, gross domestic product (GDP) per capita and population density. The results underscored the necessity of utilizing comprehensive datasets for effective modeling and highlighted the potential of machine learning to enhance environmental monitoring efforts. This research provides critical insights for policymakers and stakeholders aiming to address the growing issue of microplastic pollution in freshwater systems, providing a foundation for the development of more effective environmental management strategies.
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Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
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Researchers used machine learning to predict microplastic levels in rivers across seven coastal regions of China, identifying population density, urbanization, and industrial activity as the strongest predictors of contamination. The models successfully captured how microplastics accumulate and move through river systems using 19 different environmental and human factors. This approach could reduce the need for costly field sampling while helping target pollution management efforts where they are needed most.
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.
Machine learning approaches for predicting microplastic pollution in peatland areas
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Researchers used machine learning models to predict microplastic quantities in peatland sediments in Vietnam from easily measurable environmental parameters. The study found that pH, total organic carbon, and salinity were the most influential factors, and that Least-Square Support Vector Machines and Random Forest models could effectively predict microplastic contamination levels.
Revealing microplastic pollution hotspots in Indonesia freshwaters: A synthesis of methodological biases, anthropogenic drivers, and predictive hotspot mapping
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A generalized additive model built on open-access spatial data explained over 92% of deviance in microplastic abundance across Indonesian freshwaters, identifying population density and human activity types as the strongest predictors, and mapping high-risk unmonitored hotspots. This predictive framework offers a cost-effective tool for prioritizing microplastic monitoring and intervention in one of the world's largest plastic-polluting nations.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
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Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
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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.