Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1499-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1499-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
25 Mar 2020
Model evaluation paper |
| 25 Mar 2020
| 25 Mar 2020
PM2.5 ∕ PM10 ratio prediction based on a long short-term memory neural network in Wuhan, China
Xueling Wu
CORRESPONDING AUTHOR
Institute of Geophysics and Geomatics, China University of
Geosciences, Wuhan 430074, China
Key Laboratory of Urban Land Resources Monitoring and Simulation,
MNR, Shenzhen 518034, China
Ying Wang
Institute of Geophysics and Geomatics, China University of
Geosciences, Wuhan 430074, China
Siyuan He
Institute of Geophysics and Geomatics, China University of
Geosciences, Wuhan 430074, China
Zhongfang Wu
Institute of Geophysics and Geomatics, China University of
Geosciences, Wuhan 430074, China
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31 citations as recorded by crossref.
- Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms B. Kim et al. 10.1016/j.apr.2022.101547
- Predicting Pm10 and Pm2.5 Concentration in Container Ports: A Deep Learning Approach S. Park et al. 10.2139/ssrn.4199828
- Derived Profiles of CCN and INP Number Concentrations in the Taklimakan Desert via Combined Polarization Lidar, Sun-Photometer, and Radiosonde Observations S. Zhang et al. 10.3390/rs15051216
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- An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea H. Hong et al. 10.3390/atmos13091462
- High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model Z. Wang et al. 10.1016/j.jclepro.2021.126493
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- Predicting PM10 and PM2.5 concentration in container ports: A deep learning approach S. Park et al. 10.1016/j.trd.2022.103601
- Dynamic System Approach for Improved PM2.5 Prediction in Taiwan L. Lin et al. 10.1109/ACCESS.2020.3038853
- Short-Term Prediction of PM2.5 Using LSTM Deep Learning Methods E. Kristiani et al. 10.3390/su14042068
- Visibility Prediction over South Korea Based on Random Forest B. Kim et al. 10.3390/atmos12050552
- Integrating Fixed Monitoring Systems with Low-Cost Sensors to Create High-Resolution Air Quality Maps for the Northern China Plain Region C. Chao et al. 10.1021/acsearthspacechem.1c00174
- Hindered visibility improvement despite marked reduction in anthropogenic emissions in a megacity of southwestern China: An interplay between enhanced secondary inorganics formation and hygroscopic growth at prevailing high RH conditions F. Wan et al. 10.1016/j.scitotenv.2023.165114
- Estimation of PM10 levels using feed forward neural networks in Igdir, Turkey F. Şahin et al. 10.1016/j.uclim.2020.100721
- Air Quality Variation in Wuhan, Daegu, and Tokyo during the Explosive Outbreak of COVID-19 and Its Health Effects C. Ma & G. Kang 10.3390/ijerph17114119
- Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models C. Samuel et al. 10.1002/gj.4780
- Analyzing meteorological factors for forecasting PM10 and PM2.5 levels: a comparison between MLR and MLP models N. Talepour et al. 10.1007/s12145-024-01468-3
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- Exploring the effect of waterbodies coupled with other environmental parameters to model PM2.5 over Delhi-NCT in northwest India B. Gayen et al. 10.1016/j.apr.2022.101614
- Incorporation of Shipping Activity Data in Recurrent Neural Networks and Long Short-Term Memory Models to Improve Air Quality Predictions around Busan Port H. Hong et al. 10.3390/atmos12091172
- A Novel AI Framework for PM Pollution Prediction Applied to a Greek Port City F. Anagnostopoulos et al. 10.3390/atmos14091413
- Rain-Induced Landslide Hazard Assessment Using Inception Model and Interpretability Method—A Case Study of Zayu County, Tibet L. Su et al. 10.3390/app14125324
- An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters C. Jin et al. 10.5194/gmd-16-4137-2023
- Sustainability analysis of supply chain via particulate matter emissions prediction in China Y. Qian et al. 10.1080/13675567.2020.1870674
- Prediction of PM10 concentrations in the city of Agadir (Morocco) using non-linear autoregressive artificial neural networks with exogenous inputs (NARX) A. Adnane et al. 10.1016/j.matpr.2021.11.340
- Long-term temporal analysis of the columnar and surface aerosol relationship with planetary boundary layer height at a southern coastal site of Turkey G. Tuna Tuygun & T. Elbir 10.1016/j.apr.2020.09.008
- An extended CAViaR model for early-warning of exceedances of the air pollution standards. The case of PM10 in the city of Madrid L. Sanchis-Marco et al. 10.1016/j.apr.2022.101355
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- Semen quality as a potential susceptibility indicator to SARS-CoV-2 insults in polluted areas L. Montano et al. 10.1007/s11356-021-14579-x
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Latest update: 20 Nov 2024
Short summary
This paper presents a composite prediction system designed to improve the accuracy and applicability of PM2.5 / PM10 predictions. Based on remote sensing images, the aerosol optical thickness was obtained and corrected. Then, we selected PM2.5 / PM10-related factors from meteorological factors and air pollutants and compared the effects of several intelligent models in different prediction patterns. The results showed that the LSTM model had significant advantages in accuracy and stability.
This paper presents a composite prediction system designed to improve the accuracy and...
