Analyzing meteorological factors for forecasting PM10 and PM2.5 levels: a comparison between MLR and MLP models DOI
Nastaran Talepour, Yaser Tahmasebi Birgani, Frank J. Kelly

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

Language: Английский

Long-term projection of future climate change over the twenty-first century in the Sahara region in Africa under four Shared Socio-Economic Pathways scenarios DOI
Qingchun Guo,

Zhenfang He,

Zhaosheng Wang

et al.

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 30(9), P. 22319 - 22329

Published: Oct. 26, 2022

Language: Английский

Citations

23
Retrieving hourly seamless PM2.5 concentration across China with physically informed spatiotemporal connection DOI
Yu Ding, Siwei Li, Jia Xing

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 301, P. 113901 - 113901

Published: Dec. 2, 2023

Language: Английский

Citations

14
Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments DOI

K. Karthick,

Aruna S.K.,

R. Dharmaprakash

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(4), P. 3733 - 3748

Published: June 21, 2024

Language: Английский

Citations

5
A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City DOI Creative Commons

Zhenfang He,

Qingchun Guo, Zhaosheng Wang

et al.

Toxics, Journal Year: 2025, Volume and Issue: 13(4), P. 254 - 254

Published: March 28, 2025

Surface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), gated recurrent unit (BiGRU). The data meteorological factors pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs the models. W-CNN-BiGRU-BiLSTM demonstrated strong performance during phase, achieving an R (correlation coefficient) of 0.9952, root mean square error (RMSE) 1.4935 μg/m3, absolute (MAE) 1.2091 percentage (MAPE) 7.3782%. Correspondingly, accurate is beneficial control urban planning.

Language: Английский

Citations

0
Impact of atmospheric aerosols on air quality of three most polluted cities of Uttar Pradesh, India DOI
K. K. SINGH, Jaswant Singh, Suresh Kumar

et al.

Journal of Atmospheric and Solar-Terrestrial Physics, Journal Year: 2025, Volume and Issue: unknown, P. 106516 - 106516

Published: April 1, 2025

Language: Английский

Citations

0
Smart electronic device for air quality and exposure risk assessment DOI
Jacob Mbarndouka Taamté,

Yvette Flore Tchuente Siaka,

Nasser Nducol

et al.

Smart Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: Feb. 5, 2025

Language: Английский

Citations

0
Haze episodes elevate PM2.5-bound PAH and BFR loads without increasing bioaccessibility: Toward improved inhalation risk assessment DOI
Xian Zhang, Ziye Liu,

Bianjie Chen

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 982, P. 179640 - 179640

Published: May 12, 2025

Language: Английский

Citations

0
Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data DOI Open Access
Muhammad Daniyal, Kassim Tawiah, Sara Muhammadullah

et al.

Journal of Healthcare Engineering, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 9

Published: June 14, 2022

The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout globe suffered from this pandemic, forcing various governments apply different restrictions reduce its impact. In study, we compare time-series models with neural network autoregressive model (NNAR). study used COVID-19 data in Pakistan February 26, 2020, 18, 2022, as a training and testing set for modeling. Different were applied estimated on set, these assessed set. Based mean absolute scaled error (MAE) root square (RMSE) sets, NNAR outperformed integrated moving average (ARIMA) other competing indicating that is most appropriate forecasting. Forecasts showed cumulative confirmed cases will be 1,597,180 deaths 32,628 April 2022. We encourage Government boost immunization policy.

Language: Английский

Citations

15
Towards cleaner air in Siliguri: A comprehensive study of PM2.5 and PM10 through advance computational forecasting models for effective environmental interventions DOI
Arghadeep Bose, Indrajit Roy Chowdhury

Atmospheric Pollution Research, Journal Year: 2023, Volume and Issue: 15(2), P. 101976 - 101976

Published: Nov. 2, 2023

Language: Английский

Citations

9
PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network DOI Open Access
Li Zhang, Jinlan Liu, Yuhan Feng

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(30), P. 75104 - 75115

Published: May 22, 2023

Language: Английский

Citations

8