Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing

Zhao, Xuan; Song, Meichen; Liu, Anqi; Wang, Yiming; Wang, Tong; Cao, Jinde

doi:10.2478/jaiscr-2020-0017

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…With results that were 92 percent acceptable for one-hour prediction, the temporal dimension model was initially presented based on the improved KNN algorithm to forecast AQI values across monitoring stations. Te algorithm was utilized in conjunction with a backpropagation neural network (BPN), where it additionally considered geographic distance, to predict the outcome of air quality in the spatial dimension [20]. Tey used ML models to forecast Dhaka's air quality levels that include deep learning methods, such as LSTM, and various other techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis

Gupta

Mohta

Heda

et al. 2023

Journal of Environmental and Public Health

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An index for reporting air quality is called the air quality index (AQI). It measures the impact of air pollution on a person’s health over a short period of time. The purpose of the AQI is to educate the public on the negative health effects of local air pollution. The amount of air pollution in Indian cities has significantly increased. There are several ways to create a mathematical formula to determine the air quality index. Numerous studies have found a link between air pollution exposure and adverse health impacts in the population. Data mining techniques are one of the most interesting approaches to forecast AQI and analyze it. The aim of this paper is to find the most effective way for AQI prediction to assist in climate control. The most effective method can be improved upon to find the most optimal solution. Hence, the work in this paper involves intensive research and the addition of novel techniques such as SMOTE to make sure that the best possible solution to the air quality problem is obtained. Another important goal is to demonstrate and display the exact metrics involved in our work in such a way that it is educational and insightful and hence provides proper comparisons and assists future researchers. In the proposed work, three distinct methods—support vector regression (SVR), random forest regression (RFR), and CatBoost regression (CR)—have been utilized to determine the AQI of New Delhi, Bangalore, Kolkata, and Hyderabad. After comparing the results of imbalanced datasets, it was found that random forest regression provides the lowest root mean square error (RMSE) values in Bangalore (0.5674), Kolkata (0.1403), and Hyderabad (0.3826), as well as higher accuracy compared to SVR and CatBoost regression for Kolkata (90.9700%) and Hyderabad (78.3672%), while CatBoost regression provides the lowest RMSE value in New Delhi (0.2792) and the highest accuracy is obtained for New Delhi (79.8622%) and Bangalore (68.6860%). Regarding the dataset that was subjected to the synthetic minority oversampling technique (SMOTE) algorithm, it is noted that random forest regression provides the lowest RMSE values in Kolkata (0.0988) and Hyderabad (0.0628) and higher accuracies are obtained for Kolkata (93.7438%) and Hyderabad (97.6080%) in comparison to SVR and CatBoost regression, whereas CatBoost regression provides the highest accuracies for New Delhi (85.0847%) and Bangalore (90.3071%). This demonstrated definitely that datasets that had the SMOTE algorithm applied to them produced a higher accuracy. The novelty of this paper lies in the fact that the best regression models have been picked through thorough research by analyzing their accuracies. Moreover, unlike most related papers, dataset balancing is carried out through SMOTE. Moreover, all of the implementations have been documented via graphs and metrics, which clearly show the contrast in results and help show what actually caused the improvement in accuracy.

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Section: Literature Surveymentioning

confidence: 99%

Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis

Gupta

Mohta

Heda

et al. 2023

Journal of Environmental and Public Health

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“…We collected socio-economic data from the 2019 National Economic and Social Development Statistical Bulletin of each region. We divided the Yangtze River Delta urban agglomeration into four seasons based on the research results of Yang Mian et al(2017), as follows: spring (March-May), summer (June-August), autumn (September-November), and winter (December-February) [10][11][12][13][14][15]. ISSN 2616-5872 Vol.5, Issue 9: 51-61, DOI: 10.25236/AJEE.2023.050907…”

Section: Data Sourcementioning

confidence: 99%

Analysis of Air Quality Change Characteristics and Correlation with Meteorological Factors in Yangtze River Delta City Cluster

2023

AJEE

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This study analyzed the changes and correlations of AQI and six basic pollutants (PM 2.5 , PM 10 , SO 2 , N 2 , CO, O 3 ) in the Yangtze River Delta city cluster from 2016 to 2019 based on air pollutant concentration monitoring data and meteorological factors. The analysis used Kriging interpolation and Pearson correlation methods. The main findings were: (1) The air quality of the study area improved over time; PM 2.5 , PM 10 , N 2 and CO concentrations were higher in winter; O 3 concentration was higher in summer; SO 2 concentration was relatively stable across seasons; O 3 concentration showed an inverted "U" pattern over time, while the other pollutants showed a "U" pattern. (2) Spatially, AQI was higher in the northwest and lower in the southeast of the study area; PM 2.5 and PM 10 concentrations had high spatial similarity with AQI. (3) The correlations between each pollutant concentration and meteorological factors varied; precipitation and air temperature had significant correlations with all six pollutants; relative humidity had a more significant correlation with PM 10 ; the other factors had no significant correlations with the pollutants.

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“…Numerous artificial neural network (ANN) models exist, with common ones including RBF networks, Elman networks, and Backpropagation (BP) neural networks [13][14][15][16][17][18]. RBF networks have been used to predict urban industrial land demand and simulate smart city growth.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks

Yang,

Zhao,

et al. 2024

Preprint

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Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

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Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing

Cited by 17 publications

References 37 publications

Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis

Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis

Analysis of Air Quality Change Characteristics and Correlation with Meteorological Factors in Yangtze River Delta City Cluster

Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks

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