Matches in SemOpenAlex for { <https://semopenalex.org/work/W1968840994> ?p ?o ?g. }
- W1968840994 endingPage "128" @default.
- W1968840994 startingPage "118" @default.
- W1968840994 abstract "In the paper a novel hybrid model combining air mass trajectory analysis and wavelet transformation to improve the artificial neural network (ANN) forecast accuracy of daily average concentrations of PM2.5 two days in advance is presented. The model was developed from 13 different air pollution monitoring stations in Beijing, Tianjin, and Hebei province (Jing-Jin-Ji area). The air mass trajectory was used to recognize distinct corridors for transport of “dirty” air and “clean” air to selected stations. With each corridor, a triangular station net was constructed based on air mass trajectories and the distances between neighboring sites. Wind speed and direction were also considered as parameters in calculating this trajectory based air pollution indicator value. Moreover, the original time series of PM2.5 concentration was decomposed by wavelet transformation into a few sub-series with lower variability. The prediction strategy applied to each of them and then summed up the individual prediction results. Daily meteorological forecast variables as well as the respective pollutant predictors were used as input to a multi-layer perceptron (MLP) type of back-propagation neural network. The experimental verification of the proposed model was conducted over a period of more than one year (between September 2013 and October 2014). It is found that the trajectory based geographic model and wavelet transformation can be effective tools to improve the PM2.5 forecasting accuracy. The root mean squared error (RMSE) of the hybrid model can be reduced, on the average, by up to 40 percent. Particularly, the high PM2.5 days are almost anticipated by using wavelet decomposition and the detection rate (DR) for a given alert threshold of hybrid model can reach 90% on average. This approach shows the potential to be applied in other countries’ air quality forecasting systems." @default.
- W1968840994 created "2016-06-24" @default.
- W1968840994 creator A5034708857 @default.
- W1968840994 creator A5036111054 @default.
- W1968840994 creator A5036758801 @default.
- W1968840994 creator A5040732672 @default.
- W1968840994 creator A5072463198 @default.
- W1968840994 creator A5091910793 @default.
- W1968840994 date "2015-04-01" @default.
- W1968840994 modified "2023-10-13" @default.
- W1968840994 title "Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation" @default.
- W1968840994 cites W1965924471 @default.
- W1968840994 cites W1966313220 @default.
- W1968840994 cites W1966653299 @default.
- W1968840994 cites W1967277852 @default.
- W1968840994 cites W1967333634 @default.
- W1968840994 cites W1969296254 @default.
- W1968840994 cites W1970809251 @default.
- W1968840994 cites W1972465508 @default.
- W1968840994 cites W1973824420 @default.
- W1968840994 cites W1975079639 @default.
- W1968840994 cites W1977177161 @default.
- W1968840994 cites W1980016057 @default.
- W1968840994 cites W2000978804 @default.
- W1968840994 cites W2003831290 @default.
- W1968840994 cites W2005160638 @default.
- W1968840994 cites W2007596562 @default.
- W1968840994 cites W2009329344 @default.
- W1968840994 cites W2046780299 @default.
- W1968840994 cites W2052397874 @default.
- W1968840994 cites W2055242918 @default.
- W1968840994 cites W2055709604 @default.
- W1968840994 cites W2061232022 @default.
- W1968840994 cites W2063077402 @default.
- W1968840994 cites W2070261558 @default.
- W1968840994 cites W2079794514 @default.
- W1968840994 cites W2080651100 @default.
- W1968840994 cites W2084003102 @default.
- W1968840994 cites W2088217228 @default.
- W1968840994 cites W2092101894 @default.
- W1968840994 cites W2093503784 @default.
- W1968840994 cites W2102093423 @default.
- W1968840994 cites W2130231837 @default.
- W1968840994 cites W2132984323 @default.
- W1968840994 cites W2143426320 @default.
- W1968840994 cites W2146848957 @default.
- W1968840994 cites W2157539394 @default.
- W1968840994 cites W2163882954 @default.
- W1968840994 cites W2174785066 @default.
- W1968840994 doi "https://doi.org/10.1016/j.atmosenv.2015.02.030" @default.
- W1968840994 hasPublicationYear "2015" @default.
- W1968840994 type Work @default.
- W1968840994 sameAs 1968840994 @default.
- W1968840994 citedByCount "415" @default.
- W1968840994 countsByYear W19688409942015 @default.
- W1968840994 countsByYear W19688409942016 @default.
- W1968840994 countsByYear W19688409942017 @default.
- W1968840994 countsByYear W19688409942018 @default.
- W1968840994 countsByYear W19688409942019 @default.
- W1968840994 countsByYear W19688409942020 @default.
- W1968840994 countsByYear W19688409942021 @default.
- W1968840994 countsByYear W19688409942022 @default.
- W1968840994 countsByYear W19688409942023 @default.
- W1968840994 crossrefType "journal-article" @default.
- W1968840994 hasAuthorship W1968840994A5034708857 @default.
- W1968840994 hasAuthorship W1968840994A5036111054 @default.
- W1968840994 hasAuthorship W1968840994A5036758801 @default.
- W1968840994 hasAuthorship W1968840994A5040732672 @default.
- W1968840994 hasAuthorship W1968840994A5072463198 @default.
- W1968840994 hasAuthorship W1968840994A5091910793 @default.
- W1968840994 hasBestOaLocation W19688409941 @default.
- W1968840994 hasConcept C104317684 @default.
- W1968840994 hasConcept C105795698 @default.
- W1968840994 hasConcept C121332964 @default.
- W1968840994 hasConcept C1276947 @default.
- W1968840994 hasConcept C13662910 @default.
- W1968840994 hasConcept C139945424 @default.
- W1968840994 hasConcept C153294291 @default.
- W1968840994 hasConcept C154945302 @default.
- W1968840994 hasConcept C161067210 @default.
- W1968840994 hasConcept C178790620 @default.
- W1968840994 hasConcept C179717631 @default.
- W1968840994 hasConcept C185592680 @default.
- W1968840994 hasConcept C204241405 @default.
- W1968840994 hasConcept C204861789 @default.
- W1968840994 hasConcept C205649164 @default.
- W1968840994 hasConcept C33923547 @default.
- W1968840994 hasConcept C37381756 @default.
- W1968840994 hasConcept C39432304 @default.
- W1968840994 hasConcept C41008148 @default.
- W1968840994 hasConcept C47432892 @default.
- W1968840994 hasConcept C50644808 @default.
- W1968840994 hasConcept C55493867 @default.
- W1968840994 hasConcept C559116025 @default.
- W1968840994 hasConceptScore W1968840994C104317684 @default.
- W1968840994 hasConceptScore W1968840994C105795698 @default.
- W1968840994 hasConceptScore W1968840994C121332964 @default.
- W1968840994 hasConceptScore W1968840994C1276947 @default.