HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computer Science, Zhejiang University of Technology（浙江工业大学计算机科学学院）；Alibaba Group（阿里巴巴集团）
Accurately forecasting air quality is critical to protecting general public from lung and heart diseases. This is a challenging task due to the complicated interactions among distinct pollution sources and various other influencing factors. Existing air quality forecasting methods cannot effectively model the diffusion processes of air pollutants between cities and monitoring stations, which may suddenly deteriorate the air quality of a region. In this paper, we propose HighAir, i.e., a hierarchical graph neural network-based air quality forecasting method, which adopts an encoder-decoder architecture and considers complex air quality influencing factors, e.g., weather and land usage. Specifically, we construct a city-level graph and station-level graphs from a hierarchical perspective, which can consider city-level and station-level patterns, respectively. We design two strategies, i.e., upper delivery and lower updating, to implement the inter-level interactions, and introduce message passing mechanism to implement the intra-level interactions. We dynamically adjust edge weights based on wind direction to model the correlations between dynamic factors and air quality. We compare HighAir with the state-of-the-art air quality forecasting methods on the dataset of Yangtze River Delta city group, which covers 10 major cities within 61,500 km2. The experimental results show that HighAir significantly outperforms other methods.
Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approach
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；School of Life Sciences, Arizona State University（生命科学学院，亚利桑那州立大学）；School of Mathematical and Statistical Sciences, Arizona State University（数学与统计科学学院，亚利桑那州立大学）
FuXi-Air: Urban Air Quality Forecasting Based on Emission-Meteorology-Pollutant multimodal Machine Learning
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention（上海大气颗粒污染与防治重点实验室）；National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary（长江入海口湿地生态系统观测与研究站）；Department of Environmental Science and Engineering（环境科学与工程学院）；Shanghai Academy of AI for Science (SAIS)（上海人工智能科学研究院）；MOE Laboratory for National Development and Intelligent Governance（教育部国家发展与智能治理实验室）；Shanghai Institute for Energy and Carbon Neutrality Strategy（上海能源与碳中和战略研究院）；IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health（国际减灾与发展研究院风险互联与治理国际联合实验室）；Shanghai Institute of Eco-Chongming (SIEC)（上海生态崇明研究院）；Shanghai Environmental Monitoring Center (SEMC)（上海市环境监测中心）
WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data
NIT Meghalaya（梅加拉亚国家理工学院）；Sorbonne University（索邦大学）；Sorbonne Center for Artificial Intelligence（索邦人工智能中心）；Universiti Kebangsaan（马来西亚国民大学）；University of Philippines（菲律宾大学）；Institute of Environmental Science and Meteorology（环境科学与气象研究所）
Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings
ADAPT SFI Research Centre, School of Computer Science, Technological University Dublin（ADAPT SFI研究中心，计算机科学学院，都柏林理工大学）；ADAPT SFI Research Centre, School of Computer Science, University College Dublin（ADAPT SFI研究中心，计算机科学学院，都柏林大学学院）；School of Computer Science, Technological University Dublin（计算机科学学院，都柏林理工大学）
AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment
Wageningen University and Research（瓦赫宁根大学与研究中心）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；Weizmann Institute of Science（魏茨曼科学研究所）
Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling
Mohamed bin Zayed University of Artificial Intelligence（莫德尔·本·扎耶德人工智能大学）；Weizmann Institute of Science（魏兹曼科学研究所）；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Adaptive Time-Series Models
School of Computing, National University of Computer & Emerging Sciences (FAST)（国家计算机与新兴科学大学（FAST）计算学院）；College of Computing and Informatics, Saudi Electronic University（沙特电子大学计算与信息学院）
GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（维杰雷理工学院数学系）；SAFIR, Sorbonne University（索邦大学SAFIR中心）；Statistics Centre Abu Dhabi（阿布扎希统计中心）
Dual-Temporal LSTM with Hybrid Attention for Airline Passenger Load Factor Forecasting: Integrating Intra-Flight and Inter-Flight Booking Dynamics
Institute of Information and Communication Technology（信息与通信技术研究所）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Islamic University of Technology（伊斯兰大学）
