Forecasting of AQI (PM2.5) for the three most polluted cities in India during COVID-19 by hybrid Daubechies discrete wavelet decomposition and autoregressive (Db-DWD-ARIMA) model

Kaur, Jatinder; Singh, Sarbjit; Parmar, Kulwinder Singh

doi:10.1007/s11356-023-29501-w

Cited by 1 publication

(2 citation statements)

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“…Their experimental results outline that the model shows a good prediction performance and generalization ability. Singh et al (2023) [20] used satellite data to discuss the changes in the AQI in India during the COVID-19 pandemic; they used the most advanced statistical and deep learning methods to predict the AQI. Their results show that the short-term AQI prediction accuracy of Holt-Winter shows a better performance.…”

Section: Related Workmentioning

confidence: 99%

“…Air is essential for the survival and development of all life on Earth, and it affects a person's health as well as the economy [1]. Air quality is largely determined by natural and human activities, such as volcanic eruptions, forest fires, climate change, ozone hole, industrialization, urbanization, and transportation emissions [2]. Many pollutants can be found in the atmosphere, such as SO 2 , NO 2 , CO 2 , NO, CO, NO, PM2.5, and PM10.…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

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A Novel Combined Model for Air Quality Index Forecasting in Changchun

Chen,

Wang,

Deng

2023

Atmosphere

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With the rapid development of the economy and continuous improvement in people’s living standards, the predictions of the air quality index have attracted wide attention. In this paper, a new feature selection method (Pearson-MI) and a combined model construction method (modified inverse variance method) were proposed to study the air quality index (AQI) and its influencing factors in Changchun. The Pearson-MI method selects the factors that affect the AQI of Changchun City from many influencing factors. This method reduces the RMSE of the LSTM model and XGBoost model by 27% and 5% and the MAE by 41% and 5%, respectively. A model that combines XGBoost, SVR, RF, and LSTM was constructed using the inverse variance method to predict the air quality index of Changchun City. The modified combined model resulted in a 2% reduction in RMSE and a 0.6% reduction in MAE compared with the unmodified combined model. The numerical results of our study show that the prediction accuracy of the modified combined model is obviously higher than that of the basic model, and the prediction accuracy is further improved under the Pearson-MI feature selection.

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Section: Related Workmentioning

confidence: 99%

Section: Introduction 1background and Motivationmentioning

confidence: 99%