Matches in SemOpenAlex for { <https://semopenalex.org/work/W2973316879> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W2973316879 endingPage "109" @default.
- W2973316879 startingPage "98" @default.
- W2973316879 abstract "In recent years, the rapid development of industrial technology has been accompanied by serious environmental pollution. In the face of numerous environmental pollution problems, particulate matter (PM2.5) which has received special attention is rich in a large amount of toxic and harmful substances. Furthermore, PM2.5 has a long residence time in the atmosphere and a long transport distance, so analyzing PM2.5 distributions is an important issue for air quality prediction. Therefore, this paper proposes a method based on convolutional neural networks (CNN) and long short-term memory (LSTM) networks to analyze the spatial-temporal characteristics of PM2.5 distributions for predicting air quality in multiple cities. In experiments, the records of environmental factors in China were collected and analyzed, and three accuracy metrics (i.e., mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)) were used to evaluate the performance of the proposed method in this paper. For the evaluation of the proposed method, the performance of the proposed method was compared with other machine learning methods. The practical experimental results show that the MAE, RMSE, and MAPE of the proposed method are lower than other machine learning methods. The main contribution of this paper is to propose a deep multilayer neural network that combines the advantages of CNN and LSTM for accurately predicting air quality in multiple cities." @default.
- W2973316879 created "2019-09-26" @default.
- W2973316879 creator A5021852079 @default.
- W2973316879 creator A5045753829 @default.
- W2973316879 creator A5065077361 @default.
- W2973316879 creator A5073868078 @default.
- W2973316879 creator A5086875702 @default.
- W2973316879 date "2019-01-01" @default.
- W2973316879 modified "2023-10-14" @default.
- W2973316879 title "The Air Quality Prediction Based on a Convolutional LSTM Network" @default.
- W2973316879 cites W1968167049 @default.
- W2973316879 cites W1968988752 @default.
- W2973316879 cites W1977177161 @default.
- W2973316879 cites W2006603160 @default.
- W2973316879 cites W2056678350 @default.
- W2973316879 cites W2064675550 @default.
- W2973316879 cites W2083441060 @default.
- W2973316879 cites W2103893521 @default.
- W2973316879 cites W2138536648 @default.
- W2973316879 cites W2144354855 @default.
- W2973316879 cites W2171928131 @default.
- W2973316879 cites W2510602143 @default.
- W2973316879 cites W2760506659 @default.
- W2973316879 cites W2794659547 @default.
- W2973316879 cites W2807695771 @default.
- W2973316879 cites W2898924913 @default.
- W2973316879 cites W2901528590 @default.
- W2973316879 cites W2909911010 @default.
- W2973316879 doi "https://doi.org/10.1007/978-3-030-30952-7_12" @default.
- W2973316879 hasPublicationYear "2019" @default.
- W2973316879 type Work @default.
- W2973316879 sameAs 2973316879 @default.
- W2973316879 citedByCount "6" @default.
- W2973316879 countsByYear W29733168792020 @default.
- W2973316879 countsByYear W29733168792021 @default.
- W2973316879 countsByYear W29733168792022 @default.
- W2973316879 crossrefType "book-chapter" @default.
- W2973316879 hasAuthorship W2973316879A5021852079 @default.
- W2973316879 hasAuthorship W2973316879A5045753829 @default.
- W2973316879 hasAuthorship W2973316879A5065077361 @default.
- W2973316879 hasAuthorship W2973316879A5073868078 @default.
- W2973316879 hasAuthorship W2973316879A5086875702 @default.
- W2973316879 hasConcept C111472728 @default.
- W2973316879 hasConcept C119857082 @default.
- W2973316879 hasConcept C138885662 @default.
- W2973316879 hasConcept C154945302 @default.
- W2973316879 hasConcept C2779530757 @default.
- W2973316879 hasConcept C41008148 @default.
- W2973316879 hasConcept C81363708 @default.
- W2973316879 hasConceptScore W2973316879C111472728 @default.
- W2973316879 hasConceptScore W2973316879C119857082 @default.
- W2973316879 hasConceptScore W2973316879C138885662 @default.
- W2973316879 hasConceptScore W2973316879C154945302 @default.
- W2973316879 hasConceptScore W2973316879C2779530757 @default.
- W2973316879 hasConceptScore W2973316879C41008148 @default.
- W2973316879 hasConceptScore W2973316879C81363708 @default.
- W2973316879 hasLocation W29733168791 @default.
- W2973316879 hasOpenAccess W2973316879 @default.
- W2973316879 hasPrimaryLocation W29733168791 @default.
- W2973316879 hasRelatedWork W2337926734 @default.
- W2973316879 hasRelatedWork W2978290780 @default.
- W2973316879 hasRelatedWork W3027997911 @default.
- W2973316879 hasRelatedWork W3173182854 @default.
- W2973316879 hasRelatedWork W4287776258 @default.
- W2973316879 hasRelatedWork W4308353688 @default.
- W2973316879 hasRelatedWork W4312501200 @default.
- W2973316879 hasRelatedWork W4313050734 @default.
- W2973316879 hasRelatedWork W4320802194 @default.
- W2973316879 hasRelatedWork W4366224123 @default.
- W2973316879 isParatext "false" @default.
- W2973316879 isRetracted "false" @default.
- W2973316879 magId "2973316879" @default.
- W2973316879 workType "book-chapter" @default.