Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad

Moezzi, Seyed Mohammad Mahdi; Mohammadi, Mitra; Mohammadi, Mandana; Saloglu, Didem; Sheikholeslami, Razi

doi:10.1007/s10661-024-12567-5

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2024

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Cited by 2 publications

References 53 publications

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Enhanced Hybrid LSTM and SLAR Modeling for In-Depth Analysis of Temporal and Spatial Patterns in Compositional Data for Environmental Monitoring

Zaidi,

Dai,

Imran

et al. 2024

Process Safety and Environmental Protection

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Enhanced Hybrid LSTM and SLAR Modeling for In-Depth Analysis of Temporal and Spatial Patterns in Compositional Data for Environmental Monitoring

Zaidi,

Dai,

Imran

et al. 2024

Process Safety and Environmental Protection

View full text Add to dashboard Cite

Urban Noise Dynamics during COVID-19 Lockdown: Machine Learning Perspectives from Mashhad, Iran

Ardalan,

Mohammadi,

Bahadori

et al. 2024

Preprint

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This paper investigates the impact of human activities on noise pollution in the Mashhad metropolis, Iran, as well as the fluctuations that occurred during the COVID-19 pandemic. By leveraging a dual strategy, a comprehensive review was initiated. First, the noise level measurements collected before (March 21 to April 20, 2019) and during (March 20 to April 19, 2020) the COVID-19 quarantine period at four key intersections in Mashhad are compared. The non-parametric Wilcoxon signed test was employed to evaluate the statistical significance of the observed changes. The results showed a statistically significant reduction in the noise level during the quarantine period at every four intersections. Next, a predictive modeling algorithm named random forest (RF) was developed to predict noise pollution levels by considering time factors such as month, day, hour, and cumulative hour. The RF model achieved a high R-squared value (0.914), representing a strong correlation between predicted and actual noise levels. The predictive power of this model was demonstrated by the root mean square error (RMSE) of 0.967 and the mean absolute error (MAE) of 0.620, indicating reasonable accuracy. This study demonstrates evidence that human activities are the main cause of noise pollution in Mashhad. The findings highlight the potential benefits of urban planning strategies that reduce traffic and noise generation. Furthermore, the development of a noise prediction model using a random forest approach provides a valuable tool for future noise management efforts in urban environments.

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Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad

Cited by 2 publications

References 53 publications

Enhanced Hybrid LSTM and SLAR Modeling for In-Depth Analysis of Temporal and Spatial Patterns in Compositional Data for Environmental Monitoring

Enhanced Hybrid LSTM and SLAR Modeling for In-Depth Analysis of Temporal and Spatial Patterns in Compositional Data for Environmental Monitoring

Urban Noise Dynamics during COVID-19 Lockdown: Machine Learning Perspectives from Mashhad, Iran

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