Kill Two Birds With One Stone: A Multi-View Multi-Adversarial Learning Approach for Joint Air Quality and Weather Prediction

Han, Jindong; Liu, Hao; Zhu, Hengshu; Xiong, Hui

doi:10.1109/tkde.2023.3236423

Cited by 12 publications

(2 citation statements)

References 47 publications

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“…A dynamic-directed graph could be constructed by considering nodes as stations, and edges denote the distance of stations that denotes the edges' strength. Several studies [393,394] used a heterogeneous graph to represent the type of each station as a node type and the connection between them as an edge. They then adopt RGNNs to learn spatial and temporal correlations to predict air quality.…”

Section: Weather Forecastingmentioning

confidence: 99%

Graph Representation Learning and Its Applications: A Survey

Hoang

Jeon

You

et al. 2023

Sensors

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Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional vectors while preserving graph structure and entity relationships. Over the decades, many models have been proposed for graph representation learning. This paper aims to show a comprehensive picture of graph representation learning models, including traditional and state-of-the-art models on various graphs in different geometric spaces. First, we begin with five types of graph embedding models: graph kernels, matrix factorization models, shallow models, deep-learning models, and non-Euclidean models. In addition, we also discuss graph transformer models and Gaussian embedding models. Second, we present practical applications of graph embedding models, from constructing graphs for specific domains to applying models to solve tasks. Finally, we discuss challenges for existing models and future research directions in detail. As a result, this paper provides a structured overview of the diversity of graph embedding models.

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Section: Weather Forecastingmentioning

confidence: 99%

Graph Representation Learning and Its Applications: A Survey

Hoang

Jeon

You

et al. 2023

Sensors

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“…ML algorithms have been extensively utilized for air quality evaluation, with studies highlighting their suitability for prediction [8]. The integration of artificial intelligence (AI), including traditional ML and deep learning (DL), has yielded successful results in air quality prediction [8]- [11]. Various algorithms, such as k-nearest neighbor (KNN), have also found applications [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Toward Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management

Chatterjee,

Raju,

Navya Thara

et al. 2024

IEEE Access

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The Smart City (SC) framework has garnered global recognition for its transformative influence on society through innovative solutions. However, the extensive use of Internet of Things (IoT) devices in SCs raises concerns regarding electronic waste and resource consumption. Addressing this challenge necessitates integrating smart grid systems to safeguard SC residents' environment and wellbeing. Accurate air quality prediction is essential for informed societal decisions, safe transportation, and disaster preparedness. This study introduces a novel approach: Towards Cleaner Industries: Smart Cities' Impact on Predictive Air Quality Management (SPAM). The SPAM model utilizes a bidirectional stacking mechanism of long short-term memory neural networks, considering spatiotemporal correlations to forecast future air pollutant concentrations. Surpassing conventional methods, SPAM enhances accuracy while reducing computational complexity. Experimental findings demonstrate enhanced efficiency and accuracy, underscoring its practicality in industrial contexts. The SPAM model represents a significant advancement in promoting environmental sustainability within the SC framework.

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