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The predictive model for COVID-19 pandemic plastic pollution by using deep learning method
Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predict...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10009853/ https://www.ncbi.nlm.nih.gov/pubmed/36914765 http://dx.doi.org/10.1038/s41598-023-31416-y |
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author | Nanehkaran, Yaser A. Licai, Zhu Azarafza, Mohammad Talaei, Sona Jinxia, Xu Chen, Junde Derakhshani, Reza |
author_facet | Nanehkaran, Yaser A. Licai, Zhu Azarafza, Mohammad Talaei, Sona Jinxia, Xu Chen, Junde Derakhshani, Reza |
author_sort | Nanehkaran, Yaser A. |
collection | PubMed |
description | Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others. |
format | Online Article Text |
id | pubmed-10009853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100098532023-03-13 The predictive model for COVID-19 pandemic plastic pollution by using deep learning method Nanehkaran, Yaser A. Licai, Zhu Azarafza, Mohammad Talaei, Sona Jinxia, Xu Chen, Junde Derakhshani, Reza Sci Rep Article Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others. Nature Publishing Group UK 2023-03-13 /pmc/articles/PMC10009853/ /pubmed/36914765 http://dx.doi.org/10.1038/s41598-023-31416-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nanehkaran, Yaser A. Licai, Zhu Azarafza, Mohammad Talaei, Sona Jinxia, Xu Chen, Junde Derakhshani, Reza The predictive model for COVID-19 pandemic plastic pollution by using deep learning method |
title | The predictive model for COVID-19 pandemic plastic pollution by using deep learning method |
title_full | The predictive model for COVID-19 pandemic plastic pollution by using deep learning method |
title_fullStr | The predictive model for COVID-19 pandemic plastic pollution by using deep learning method |
title_full_unstemmed | The predictive model for COVID-19 pandemic plastic pollution by using deep learning method |
title_short | The predictive model for COVID-19 pandemic plastic pollution by using deep learning method |
title_sort | predictive model for covid-19 pandemic plastic pollution by using deep learning method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10009853/ https://www.ncbi.nlm.nih.gov/pubmed/36914765 http://dx.doi.org/10.1038/s41598-023-31416-y |
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