Prediction of high resolution spatial-temporal air pollutant map from big data sources

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9196, Issue , 2015, Pages 273-282

  Li, Yingyu ^a   Zhu, Yifang ^b   Yin, Wotao ^b   Liu, Yang ^c   Shi, Guangming ^a   Han, Zhu ^d  

^a XIDIAN UNIVERSITY   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c EMORY UNIVERSITY   (United States)

^d UNIVERSITY OF HOUSTON   (United States)

Author keywords

Air pollution; Big data; Heterogeneous data sources; High resolution spatial temporal concentration map; INLA; SOCP; Sparsity; SPDE

Indexed keywords

AIR QUALITY; APPROXIMATION ALGORITHMS; BIG DATA; COMPUTATIONAL EFFICIENCY; MARKOV PROCESSES; MONTE CARLO METHODS; OPTIMIZATION; POLLUTION; POLLUTION DETECTION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

HETEROGENEOUS DATA SOURCES; INLA; SOCP; SPARSITY; SPATIAL TEMPORALS; SPDE;

AIR POLLUTION;

EID: 84947205315     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-22047-5_22     Document Type: Conference Paper

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