Analysis of classification and clustering techniques for ambient AQI using machine learning algorithms

R, Senthil Kumar; Arulanandham, Anidha; Arumugam, S.; Dinesh, G.; Thirukkumaran, R.; Subashmoorthy, R

doi:10.1109/icssit53264.2022.9716359

Cited by 9 publications

(3 citation statements)

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“…Senthil Kumar et al conducted a detailed analysis and comparison using a total of 11 algorithms, including Bayesian models, regression models, ensemble models, instance-based models, and tree-based models, for predicting environmental air quality indices in southern Indian cities. The research indicated that ensemble classification models and density-based clustering methods provided better results in handling air quality data [29].…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, various models were developed, including Linear Regression (LR), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, ensemble models like Random Forest (RF), Extreme Gradient Boosting (XGBT), and Light Gradient Boosting Machine (LGBM). Additionally, considering prior research on single-model prediction, where the LSTM model was found to be suitable for time-series forecasting and widely applied in air quality prediction, and SVR displayed good performance in AQI prediction, being the optimal model in several studies [28,29], this paper further combined the independent SVR and LSTM models to construct an LSTM-SVR hybrid model. These models were employed to predict the Air Quality Index for six urban agglomerations in China, and the performance of the models in predicting different urban agglomerations was compared.…”

Section: Related Workmentioning

confidence: 99%

“…The BA-LSSVM algorithm, constructed by Wu et al, considers air pollutant factors such as PM 2.5 , PM 10 , CO, SO 2 , NO 2 , and O 3 and takes predicted values from each component to obtain AQI predictions [16]. Senthil Kumar et al not only used six types of pollutants but also added some nitrogen oxides and benzene pollutants to predict the environmental air quality index of cities in southern India [29]. In China's air quality index composition system, the composition of AQI is determined based on the air quality sub-indices of these six pollutants.…”

Section: Data Sourcesmentioning

confidence: 99%

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Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Zhang,

Duan,

Sun

et al. 2023

Atmosphere

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Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%