Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollutio...

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Bibliographic Details
Published in:Sustainability 2022-03, Vol.14 (5), p.3106
Main Authors: Deng, Guoqu, Chen, Hu, Xie, Bo, Wang, Mengtian
Format: Article
Language:English
Subjects:
Air pollution
Analysis
Back propagation networks
Birth rate
Disasters
Emissions
Environmental hazards
Evaluation
Expenditures
Genetic algorithms
Genetic analysis
Hydrocarbons
Industrial development
Machine learning
Methods
Mortality
Neural networks
Outdoor air quality
Pollutants
Pollution index
Population
Principal components analysis
Regional development
Regional planning
Regions
Risk assessment
Root-mean-square errors
Social change
Support vector machines
Sustainability
Sustainable development
Temperature
Trends
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Summary:Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollution disaster risk. This research has integrated principal component analysis (PCA), a genetic algorithm (GA) and a backpropagation (BP) neural network to evaluate the regional disaster risk. Hazard risk, hazard-laden environment sensitivity, hazard-bearing body vulnerability and disaster resilience were used to measure the degree of disaster risk. The main findings were: (1) the air pollution disaster risk index of Liaoning Province, Beijing, Shanghai and Guangdong Province increased year by year from 2010 to 2019; (2) the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of each regional air pollution disaster risk index in 2019, as predicted by the PCA-GA-BP neural network, were 0.607, 0.317 and 20.3%, respectively; (3) the predicted results were more accurate than those using a PCA-BP neural network, GA-BP neural network, traditional BP neural network, support vector regression (SVR) or extreme gradient boosting (XGBoost), which verified that machine learning could be used as a method of air pollution disaster risk assessment to a considerable extent.
ISSN:2071-1050
2071-1050
DOI:10.3390/su14053106