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COVID-19 pandemic microplastics environmental impacts predicted by deep random forest (DRF) predictive model
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A deep random forest algorithm outperformed conventional machine learning methods in predicting microplastic pollution during the COVID-19 pandemic in Tehran, using data on household, hospital, clinic, and unknown-origin waste streams.
Microplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran. The research introduces a rigorous comparative analysis that evaluates the predictive prowess of the Deep Random Forest algorithm and established benchmarks, such as Random Forest, Decision Trees, Gradient Boosting, AdaBoost, and Support Vector Machine. The evaluation process encompasses a meticulous 70–30 training–testing split of the main data set. Performance is assessed by analysis metrics, including ROC and statistical errors. The primary data set encompasses distinct categories, including household wastes, hospital wastes, clinics wastes, and unknown-originated susceptible waste which is categorized in Infected items, PPEs, SUPs, Test kits, Medical packages, Unknown-originated pandemic mircoplastic waste. Deliberately, this data set was partitioned into training and testing subsets, ensuring the robustness and reliability of subsequent analyses. Approximately 70% of the main database was allocated to the training data set, with the remaining 30% constituting the testing data set. The findings underscore the proposed algorithm’s supremacy, boasting an impressive AUC = 0.941. This exceptional score reflects the model’s precision in categorizing microplastics. These results have profound implications for environmental management and public health during pandemics. The study positions the proposed model as a potent tool for microplastic pollution prediction, encouraging further research to refine predictive models and tap into new data sources for a more comprehensive understanding of microplastic dynamics in urban settings.
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Researchers built a deep learning model to predict how pandemic-related plastic waste — masks, gloves, and sanitizer bottles — would spread as pollution across Iranian megacities during COVID-19. Their neural network outperformed six other modeling methods, offering a tool for governments to manage hazardous plastic waste during future health crises.
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Scientists tested whether AI could predict microplastic pollution levels on coastlines using environmental data, instead of relying on slow, expensive lab testing. A simpler, well-established computer model (Random Forest) turned out to be much more accurate than a fancier "deep learning" AI system at this task. This matters because faster, cheaper ways to track microplastic pollution could help communities identify contaminated areas sooner—which is important since microplastics are increasingly linked to health concerns as they make their way into seafood and drinking water.
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Researchers developed a methodology using random decision forest classifiers for the fast identification and monitoring of microplastics in environmental samples. The approach provides a machine learning-based tool to accelerate microplastic detection and reduce the analytical burden of characterising particles across diverse environmental matrices.
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This review examines how machine learning techniques including neural networks and random forests are being applied to microplastic detection, classification, and ecological risk assessment, demonstrating faster and more accurate results than traditional analytical methods. The authors identify data standardization and model interpretability as key challenges for broader adoption.
Environmental impact analysis of nano-level COVID-19 waste disposal using a comparative deep multi-layer perceptron algorithm
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