Matches in SemOpenAlex for { <https://semopenalex.org/work/W3201590941> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W3201590941 abstract "Air pollution poses a serious threat to sustainable environmental conditions in the 21st century. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors ranging from artificial emissions to natural phenomena are known to be primary causal agents or influencers behind rising air pollution levels. However, the lack of large scale data involving the major artificial and natural factors has hindered the research on the causes and relations governing the variability of the different air pollutants. Through this work, we introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. We also introduce a transformer based model - cosSquareFormer, for the problem of pollutant level estimation and forecasting. Our model outperforms most of the benchmark models for this task. We also analyze and explore the dataset through our model and other methodologies to bring out important inferences which enable us to understand the dynamics of the casual agents at a deeper level. Through our paper, we seek to provide a great set of foundations for further research into this domain that will demand critical attention of ours in the near future." @default.
- W3201590941 created "2021-09-27" @default.
- W3201590941 creator A5017026210 @default.
- W3201590941 creator A5041256413 @default.
- W3201590941 creator A5084554645 @default.
- W3201590941 date "2022-07-01" @default.
- W3201590941 modified "2023-10-16" @default.
- W3201590941 title "Deciphering Environmental Air Pollution with Large Scale City Data" @default.
- W3201590941 cites W1678356000 @default.
- W3201590941 cites W1959608418 @default.
- W3201590941 cites W2064675550 @default.
- W3201590941 cites W2294879709 @default.
- W3201590941 cites W2296609147 @default.
- W3201590941 cites W2626778328 @default.
- W3201590941 cites W2766736793 @default.
- W3201590941 cites W2784284461 @default.
- W3201590941 cites W2809533013 @default.
- W3201590941 cites W2891772212 @default.
- W3201590941 cites W2897932070 @default.
- W3201590941 cites W2962994101 @default.
- W3201590941 cites W2970631142 @default.
- W3201590941 cites W2991648381 @default.
- W3201590941 cites W3002709689 @default.
- W3201590941 cites W3009513956 @default.
- W3201590941 cites W3021721382 @default.
- W3201590941 cites W3035717876 @default.
- W3201590941 cites W3046975334 @default.
- W3201590941 cites W3126170988 @default.
- W3201590941 cites W3196756515 @default.
- W3201590941 doi "https://doi.org/10.24963/ijcai.2022/694" @default.
- W3201590941 hasPublicationYear "2022" @default.
- W3201590941 type Work @default.
- W3201590941 sameAs 3201590941 @default.
- W3201590941 citedByCount "0" @default.
- W3201590941 crossrefType "proceedings-article" @default.
- W3201590941 hasAuthorship W3201590941A5017026210 @default.
- W3201590941 hasAuthorship W3201590941A5041256413 @default.
- W3201590941 hasAuthorship W3201590941A5084554645 @default.
- W3201590941 hasBestOaLocation W32015909411 @default.
- W3201590941 hasConcept C159985019 @default.
- W3201590941 hasConcept C166957645 @default.
- W3201590941 hasConcept C178790620 @default.
- W3201590941 hasConcept C185592680 @default.
- W3201590941 hasConcept C192562407 @default.
- W3201590941 hasConcept C205649164 @default.
- W3201590941 hasConcept C2776608160 @default.
- W3201590941 hasConcept C2778755073 @default.
- W3201590941 hasConcept C2781426162 @default.
- W3201590941 hasConcept C41008148 @default.
- W3201590941 hasConcept C559116025 @default.
- W3201590941 hasConcept C58640448 @default.
- W3201590941 hasConceptScore W3201590941C159985019 @default.
- W3201590941 hasConceptScore W3201590941C166957645 @default.
- W3201590941 hasConceptScore W3201590941C178790620 @default.
- W3201590941 hasConceptScore W3201590941C185592680 @default.
- W3201590941 hasConceptScore W3201590941C192562407 @default.
- W3201590941 hasConceptScore W3201590941C205649164 @default.
- W3201590941 hasConceptScore W3201590941C2776608160 @default.
- W3201590941 hasConceptScore W3201590941C2778755073 @default.
- W3201590941 hasConceptScore W3201590941C2781426162 @default.
- W3201590941 hasConceptScore W3201590941C41008148 @default.
- W3201590941 hasConceptScore W3201590941C559116025 @default.
- W3201590941 hasConceptScore W3201590941C58640448 @default.
- W3201590941 hasLocation W32015909411 @default.
- W3201590941 hasLocation W32015909412 @default.
- W3201590941 hasOpenAccess W3201590941 @default.
- W3201590941 hasPrimaryLocation W32015909411 @default.
- W3201590941 hasRelatedWork W1563128218 @default.
- W3201590941 hasRelatedWork W1983180063 @default.
- W3201590941 hasRelatedWork W2001962162 @default.
- W3201590941 hasRelatedWork W2369952139 @default.
- W3201590941 hasRelatedWork W2391289919 @default.
- W3201590941 hasRelatedWork W2469724257 @default.
- W3201590941 hasRelatedWork W2742958341 @default.
- W3201590941 hasRelatedWork W4220739838 @default.
- W3201590941 hasRelatedWork W4249422084 @default.
- W3201590941 hasRelatedWork W2183681141 @default.
- W3201590941 isParatext "false" @default.
- W3201590941 isRetracted "false" @default.
- W3201590941 magId "3201590941" @default.
- W3201590941 workType "article" @default.