Prediction of PM2.5 concentration in Ulaanbaatar with deep learning models

Suriya,; Natsagdorj, Narantsogt; Aorigele,; Zhou, Haijun; Sachurila,

doi:10.1016/j.uclim.2022.101357

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…In their proposal, Zhang et al [36] combined CNN with spatial-temporal attention and residual learning to create a hybrid deep learning network for short-term PM 2.5 concentration prediction. Natsagdorj et al [37] implemented two deep learning frameworks using Bayesian optimized LSTM and CNN-LSTM to forecast daily PM 2.5 levels in Ulaanbaatar, Mongolia. They collected hourly Himawari8 aerosol optical depth, PM 2.5 concentration, and meteorological data to train their prediction models.…”

Section: A Related Workmentioning

confidence: 99%

CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

Dey,

Dev,

Phelan

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 (µg/m 3 ) concentration prediction may help reduce health concerns and provide early warnings. To better understand the air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutant due to their success in pattern recognition, however, these models were unsuitable for multi-site, long-term predictions, particularly in regards to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep network, including a bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and a shallow model represented by fully connected layers (FC), to create a hybrid forecasting system. It can effectively capture the complex relationships between air pollutants and various influencing factors to predict hourly PM2.5 concentrations in multiple monitoring sites based in China. The best root mean square error (RMSE) achieved was 22.0 µg/m 3 (long-term) and 6.2 µg/m 3 (short-term), with mean absolute error (MAE) values of 3.4 µg/m 3 (long-term) and 2.2 µg/m 3 (short-term). Additionally, the correlation coefficient (R 2 ) reached 0.96 (longterm) and 0.83 (short-term) across six monitoring sites. These results demonstrate that CombineDeepNet enhances prediction accuracy compared to popular deep learning methods. Therefore, CombineDeepNet proves to be a important framework for predicting PM2.5 concentration.

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Section: A Related Workmentioning

confidence: 99%

CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

Dey,

Dev,

Phelan

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Yan et al [81] employed CNNs to effectively handle various data types (satellite imagery and meteorological and sensor data), demonstrating the models' prowess in extracting complex spatial features. Another study by Suriya et al [124] leveraged a CNN-based model to predict PM2.5 concentrations using satellite imagery coupled with meteorological data, outshining traditional machine learning models in terms of prediction accuracy.…”

Section: Introduction Of An Ensemble Of Multifeatured and Multi-model...mentioning

confidence: 99%

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Abimannan,

El-Alfy,

Hussain

et al. 2023

Sustainability

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Systems for monitoring air quality are essential for reducing the negative consequences of air pollution, but creating real-time systems encounters several challenges. The accuracy and effectiveness of these systems can be greatly improved by integrating federated learning and multi-access edge computing (MEC) technology. This paper critically reviews the state-of-the-art methodologies for federated learning and MEC-enabled air quality monitoring systems. It discusses the immense benefits of federated learning, including privacy-preserving model training, and MEC, such as reduced latency and improved response times, for air quality monitoring applications. Additionally, it highlights the challenges and requirements for developing and implementing real-time air quality monitoring systems, such as data quality, security, and privacy, as well as the need for interpretable and explainable AI-powered models. By leveraging such advanced techniques and technologies, air monitoring systems can overcome various challenges and deliver accurate, reliable, and timely air quality predictions. Moreover, this article provides an in-depth analysis and assessment of the state-of-the-art techniques and emphasizes the need for further research to develop more practical and affordable AI-powered decentralized systems with improved performance and data quality and security while ensuring the ethical and responsible use of the data to support informed decision making and promote sustainability.

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Insights into airborne particulate matter: artificial intelligence-driven PM2.5 modelling in Hyderabad district, India

A K,

Mathew

2024

Stoch Environ Res Risk Assess

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Prediction of PM2.5 concentration in Ulaanbaatar with deep learning models

Cited by 11 publications

References 51 publications

CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Insights into airborne particulate matter: artificial intelligence-driven PM2.5 modelling in Hyderabad district, India

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