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Efficient inference of large-scale air quality using a lightweight ensemble predictor

文献类型：期刊论文

作者Wang, Peixiao3,4; Zhang, Hengcai3,4; Liu, Jie2; Lu, Feng3,4; Zhang, Tong1
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2024-12-06
卷号N/A
关键词Air quality prediction computationally efficient model large-scale sparse scenario
DOI10.1080/13658816.2024.2437044
产权排序1
文献子类Article ; Early Access
英文摘要Accurate and efficient air quality prediction is crucial for public health protection and environmental sustainability. While numerous grid-based and graph-based prediction models have been developed, they encounter challenges in large-scale scenarios: (1) Grid-based models, though computationally efficient, have limited prediction accuracy in large-scale sparse scenarios; (2) Graph-based models, despite higher prediction accuracy, suffer from significant computational inefficiencies when dealing with a large number of sensors, i.e. graph nodes. To address these issues, we propose a Lightweight Ensemble Predictor (LiEnPred) for efficient air quality prediction in large-scale sparse scenarios. First, we present a data structure transformation algorithm that converts sparse monitoring sensors from graph structures to compact grid structures, preserving the connections between graph nodes. Next, we present a lightweight parameter-shared spatio-temporal dilation convolution network that efficiently captures spatio-temporal dependencies in air quality data without significantly increasing computation time or parameter scale. In our experiments, we collected air quality data from over 2000 sensors across China over the past three years and evaluated LiEnPred's prediction performance in large-scale scenarios using PM2.5 and NO2 concentration data. The experimental results demonstrate that the proposed LiEnPred model matches or exceeds the predictive accuracy of eight baselines with faster time efficiency and fewer model parameters.
WOS关键词NETWORKS
WOS研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS记录号WOS:001373384000001
源URL[http://ir.igsnrr.ac.cn/handle/311030/210435]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Zhang, Hengcai
作者单位1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China
2.Univ Jinan, Sch Informat Sci & Engn, Jinan, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Peixiao,Zhang, Hengcai,Liu, Jie,et al. Efficient inference of large-scale air quality using a lightweight ensemble predictor[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2024,N/A.
APA Wang, Peixiao,Zhang, Hengcai,Liu, Jie,Lu, Feng,&Zhang, Tong.(2024).Efficient inference of large-scale air quality using a lightweight ensemble predictor.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A.
MLA Wang, Peixiao,et al."Efficient inference of large-scale air quality using a lightweight ensemble predictor".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2024).

入库方式： OAI收割

来源：地理科学与资源研究所

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