Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

Pande, Chaitanya Baliram; Kushwaha, Nand Lal; Alawi, Omer A.; Sammen, Saad Sh; Sidek, Lariyah Mohd; Yaseen, Zaher Mundher; Pal, Subodh Chandra; Katipoğlu, Okan Mert

doi:10.1016/j.envpol.2024.124040

Environmental Pollution

2024

DOI: 10.1016/j.envpol.2024.124040

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Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

Chaitanya Baliram Pande,

Nand Lal Kushwaha,

Omer A. Alawi

et al.

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Cited by 4 publications

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TDPM-CNN: a comprehensive daytime and nighttime PM2.5 estimation method using multi-kernel convolutional neural networks

Chang,

Sarveshwaran,

et al. 2024

Earth Sci Inform

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TDPM-CNN: a comprehensive daytime and nighttime PM2.5 estimation method using multi-kernel convolutional neural networks

Chang,

Sarveshwaran,

et al. 2024

Earth Sci Inform

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Spatiotemporal characteristics and driven forces of nitrogen flow of dairy farms in China: Based on a comprehensive model combined with production practices and embedding localized parameters

Yang,

Zhang,

Wang

et al. 2024

Science of The Total Environment

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Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

He,

Guo

2024

Atmosphere

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Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other seven models, achieving an R (correlation coefficient) of 0.9686 and an RMSE (root mean square error) of 4.6491 μg/m3. The RMSE values of CNN, GRU and LSTM models are 57.00%, 35.98% and 32.78% higher than that of the CNN-GRU-LSTM method, respectively. The forecasting results reveal that the CNN-GRU-LSTM predictor remarkably improves the performances of benchmark CNN, GRU and LSTM models in overall forecasting. This research method provides a new perspective for predictive forecasting of ambient air pollution PM2.5 concentrations. The research results of the predictive model provide a scientific basis for air pollution prevention and control.

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Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

Cited by 4 publications

References 77 publications

TDPM-CNN: a comprehensive daytime and nighttime PM2.5 estimation method using multi-kernel convolutional neural networks

TDPM-CNN: a comprehensive daytime and nighttime PM2.5 estimation method using multi-kernel convolutional neural networks

Spatiotemporal characteristics and driven forces of nitrogen flow of dairy farms in China: Based on a comprehensive model combined with production practices and embedding localized parameters

Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

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