A long short-term memory approach to predicting air quality based on social media data

Zhai, Weixin; Cheng, Chengqi

doi:10.1016/j.atmosenv.2020.117411

Cited by 28 publications

(12 citation statements)

References 45 publications

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“…For example, Fong et al applied Long Short-Term Memory (LSTM) combined with transfer learning and pre-trained neural networks [ 17 ] to predict air pollutants in the next day using meteorological and air pollutant’s concentration data of Macau. Zhai and Cheng performed a one-day forecast implementing LSTM on air quality, meteorological and social media data [ 19 ]. Another work by Yang et al proposed hybrid Convolutional Neural Network (CNN)-LSTM and CNN-Gated Recurrent Unit (GRU) models to predict PM 10 and PM 2.5 for the next seven days in Seoul using air pollution and meteorological data [ 37 ].…”

Section: Related Workmentioning

confidence: 99%

Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid

2022

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Nitrogen dioxide is one of the pollutants with the most significant health effects. Advanced information on its concentration in the air can help to monitor and control further consequences more effectively, while also making it easier to apply preventive and mitigating measures. Machine learning technologies with available methods and capabilities, combined with the geospatial dimension, can perform predictive analyses with higher accuracy and, as a result, can serve as a supportive tool for productive management. One of the most advanced machine learning algorithms, Bidirectional convolutional LSTM, is being used in ongoing work to predict the concentration of nitrogen dioxide. The model has been validated to perform more accurate spatiotemporal analysis based on the integration of temporal and geospatial factors. The analysis was carried out according to two scenarios developed on the basis of selected features using data from the city of Madrid for the periods January-June 2019 and January-June 2020. Evaluation of the model’s performance was conducted using the Root Mean Square Error and the Mean Absolute Error which emphasises the superiority of the proposed model over the reference models. In addition, the significance of a feature selection technique providing improved accuracy was underlined. In terms of execution time, due to the complexity of the Bidirectional convolutional LSTM architecture, convergence and generalisation of the data took longer, resulting in the superiority of the reference models.

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Section: Related Workmentioning

confidence: 99%

Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid

2022

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“…Similarly, Zhai and Cheng [167] proposed an LSTM model for predicting air quality using Weibo posts, Ali et al [11] designed a biLSTM model for traffic accident detection using Twitter and Facebook. Beskow et al [20] designed an LSTM-CNN model for detecting and characterizing political memes, and Ertugrul et al [43] developed an LSTM model for predicting future protests.…”

Section: The Deep Learning Trendmentioning

confidence: 99%

Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

2022

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The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the “big picture”—the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime , Disasters , Finance , Physical Environment , Politics , and Health and Population . We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.

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“…Therefore, these machine learning methods, which mainly focus on static data, lose time information during the prediction process. To deal with this kind of spatio-temporal data, several neural network models and their hybrid model have been applied to air quality prediction and achieved good accuracy, such as CNN, RNN and LSTM (Maciąg et al, 2019;Wen et al, 2019;Zhai and Cheng, 2020).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai

Liu

Wang

et al. 2021

Aerosol Air Qual. Res.

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In recent years, the harmfulness of air pollutants to human health and the environment have received widespread attention. Accurate prediction of air quality is essential to air quality management and policy development, however, the seasonal differences and the diurnal variations of air pollutants have not been fully investigated by traditional forecast models. Furthermore, the available spatio-temporal data for the problem have not been well processed to capture informative predictive patterns from past data. This paper proposes a Staging evolving spiking neural network model (Staging-eSNN), which uses a time series clustering algorithm to distinguish seasonal differences with diurnal variations of PM2.5 concentration. Based on this pretreatment, we use the Staging-eSNN model to predict PM2.5 concentration in the next 1, 3, 6, 12 and 24 hour in Beijing and Shanghai areas. Various evaluation indicators for prediction performance show that the Staging-eSNN model achieves appealing performance than SVR, RF and other eSNN models.

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A long short-term memory approach to predicting air quality based on social media data

Cited by 28 publications

References 45 publications

Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid

Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid

Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai

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