Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model

Kumbalaparambi, Thushara Sudheish; Menon, Ratish; Radhakrishnan, Vishnu P; Nair, Vinod P

doi:10.1007/s11356-022-22836-w

Cited by 4 publications

(2 citation statements)

References 31 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Urban air quality is gradually deteriorating and becoming toxic [ [1] , [2] , [3] ]. Urbanization and growing economic activities are widely perceived to cause these environmental problems [ [4] , [5] , [6] ].…”

Section: Introductionmentioning

confidence: 99%

A GIS-based approach to determining optimal location for decentralized inner city smart filters: Toward net zero cities

Alshuwaikhat,

Basheer,

AlAtiq

2024

Heliyon

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A GIS-based approach to determining optimal location for decentralized inner city smart filters: Toward net zero cities

Alshuwaikhat,

Basheer,

AlAtiq

2024

Heliyon

View full text Add to dashboard Cite

“…To address these challenges, we have designed a novel deep learning model named the Meteorological Sparse Autoencoding Transformer (MSAFormer), based on the Transformer architecture and sparse autoencoding technology. The Transformer architecture, initially designed for natural language processing tasks [55], is adept at handling long-term dependencies in the input data-thanks to its powerful self-attention mechanism-and has exhibited excellent performance in many other fields [56], including environmental science [57]. On the other hand, sparse autoencoding is an effective feature learning technique that can automatically extract and learn significant features from high-dimensional data [58].…”

Section: Introductionmentioning

confidence: 99%

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

Wang

Zhang

2023

Atmosphere

View full text Add to dashboard Cite

The accurate prediction of PM2.5 concentration, a matter of paramount importance in environmental science and public health, has remained a substantial challenge. Conventional methodologies for predicting PM2.5 concentration often grapple with capturing complex dynamics and nonlinear relationships inherent in multi-station meteorological data. To address this issue, we have devised a novel deep learning model, named the Meteorological Sparse Autoencoding Transformer (MSAFormer). The MSAFormer leverages the strengths of the Transformer architecture, effectively incorporating a Meteorological Sparse Autoencoding module, a Meteorological Positional Embedding Module, and a PM2.5 Prediction Transformer Module. The Sparse Autoencoding Module serves to extract salient features from high-dimensional, multi-station meteorological data. Subsequently, the Positional Embedding Module applies a one-dimensional Convolutional Neural Network to flatten the sparse-encoded features, facilitating data processing in the subsequent Transformer module. Finally, the PM2.5 Prediction Transformer Module utilizes a self-attention mechanism to handle temporal dependencies in the input data, predicting future PM2.5 concentrations. Experimental results underscore that the MSAFormer model achieves a significant improvement in predicting PM2.5 concentrations in the Haidian district compared to traditional methods. This research offers a novel predictive tool for the field of environmental science and illustrates the potential of deep learning in the analysis of environmental meteorological data.

show abstract

A Perceived Risk Index Leveraging Social Media Data: Assessing Severity of Fire on Microblogging

De Maio,

Fenza,

Gallo

et al. 2024

Cogn Comput

View full text Add to dashboard Cite

Fires represent a significant threat to the environment, infrastructure, and human safety, often spreading rapidly with wide-ranging consequences such as economic losses and life risks. Early detection and swift response to fire outbreaks are crucial to mitigating their impact. While satellite-based monitoring is effective, it may miss brief or indoor fires. This paper introduces a novel Perceived Risk Index (PRI) that, complementing satellite data, leverages social media data to provide insights into the severity of fire events. In the light of the results of statistical analysis, the PRI incorporates the number of fire-related tweets and the associated emotional expressions to gauge the perceived risk. The index’s evaluation involves the development of a comprehensive system that collects, classifies, annotates, and correlates social media posts with satellite data, presenting the findings in an interactive dashboard. Experimental results using diverse datasets of real-fire tweets demonstrate an average best correlation of 77% between PRI and the brightness values of fires detected by satellites. This correlation extends to the real intensity of the corresponding fires, showcasing the potential of social media platforms in furnishing information for emergency response and decision-making. The proposed PRI proves to be a valuable tool for ongoing monitoring efforts, having the potential to capture data on fires missed by satellites. This contributes to the development to more effective strategies for mitigating the environmental, infrastructural, and safety impacts of fire events.

show abstract

Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model

Cited by 4 publications

References 31 publications

A GIS-based approach to determining optimal location for decentralized inner city smart filters: Toward net zero cities

A GIS-based approach to determining optimal location for decentralized inner city smart filters: Toward net zero cities

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

A Perceived Risk Index Leveraging Social Media Data: Assessing Severity of Fire on Microblogging

Contact Info

Product

Resources

About