Liping Chen, Arkan K. S. Sabonchi, Yaser A. Nanehkaran
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引用次数: 0
Abstract
Background
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.
Methods
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.
Results
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.
Conclusions
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.
期刊介绍:
ESEU is an international journal, focusing primarily on Europe, with a broad scope covering all aspects of environmental sciences, including the main topic regulation.
ESEU will discuss the entanglement between environmental sciences and regulation because, in recent years, there have been misunderstandings and even disagreement between stakeholders in these two areas. ESEU will help to improve the comprehension of issues between environmental sciences and regulation.
ESEU will be an outlet from the German-speaking (DACH) countries to Europe and an inlet from Europe to the DACH countries regarding environmental sciences and regulation.
Moreover, ESEU will facilitate the exchange of ideas and interaction between Europe and the DACH countries regarding environmental regulatory issues.
Although Europe is at the center of ESEU, the journal will not exclude the rest of the world, because regulatory issues pertaining to environmental sciences can be fully seen only from a global perspective.