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. 2023 Mar 13;13(1):4126.
doi: 10.1038/s41598-023-31416-y.

The predictive model for COVID-19 pandemic plastic pollution by using deep learning method

Yaser A Nanehkaran  1 , Zhu Licai  1 , Mohammad Azarafza  2 , Sona Talaei  3 , Xu Jinxia  1 , Junde Chen  4 , Reza Derakhshani  5
Affiliations

The predictive model for COVID-19 pandemic plastic pollution by using deep learning method

Yaser A Nanehkaran et al. Sci Rep. .

Abstract

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The number of new daily infected cases of COVID-19 in Iran's metropolises.
Figure 2
Figure 2
The processing flowchart of the DNN predictive model.
Figure 3
Figure 3
The amount of household plastic waste caused by COVID-19 in Iran's metropolises.
Figure 4
Figure 4
The amount of hospital plastic waste caused by COVID-19 in Iran's metropolises.
Figure 5
Figure 5
The DNN model performance evolution for COVID-19 pandemic plastic in the first 100 epoch.
Figure 6
Figure 6
The error rate and confusion matrix for DNN predictive model.
Figure 7
Figure 7
The regression metrics for DNN predictive model: (a) Ahvaz, (b) Esfahan, (c) Karaj, (d) Mashhad, (e) Qum, (f) Shiraz, (g) Tabriz, (h) Tehran.
See this image and copyright information in PMC

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