Matches in SemOpenAlex for { <https://semopenalex.org/work/W2183683505> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W2183683505 abstract "The objective of the present study is to build different models forecasting the daily mean relative humidity (MRH) values in China with the help of the meteorological parameters. A back-propagation artificial neural network (BPANN) models was employed to identify the relationship between meteorological factors and the relative humidity in China. Weather data 1-day lag was the input layer variables, including (1) the highest atmospheric pressure, (2) the lowest atmospheric pressure, (3) the average atmospheric pressure, (4) the average temperature, (5) the highest temperature, (6) the lowest temperature, (7) precipitation, (8) the average wind speed, (9) the maximum wind speed (the average wind speed over 10 minutes), (10) the utmost wind speed, (11) hours of sunlight, (12) the relative humidity. Experimental results: in the validation period for 1-day lead, the comparison of the prediction performance efficiency of the BPANN models indicated that the BPANN models with trainbr algorithm was superior to the remaining two ones (trainlm and traingdx) in forecasting the relative humidity time series in term of correlation coefficient (R). During the training and testing periods for 1-day lead, the best performance for the given problem was arid area, followed by semi-arid area, semi-humid area, and humid area respectively. The possible cause for the results was that the impact of these factors on the relative humidity in arid area was the largest, followed by semi-arid area, semi-humid area, and humid area, respectively. From the prediction results of MRHextrema, humid area was the first; semi-arid area was the second; semi-humid area was the third; and arid area was the fourth. From the prediction results of MRHextrema, trainbr algorithm was the best in arid area, semi-humid area, and humid area; but trainlm was the best in semi-arid area. So trainbr algorithm was further employed to predict MRH for 2, 3 or 4-day lead at Urumqi City. From the training and testing effects, 1-day lead was the best, followed by 2, 3 or 4-day lead respectively. In the prediction results of MRHextrema, the best was 2-day lead; the second was 3-day lead; the third was 1-day lead; and the fourth was 4-day lead. The BPANN model results will assist researchers determining meteorological parameters to forecast MRH." @default.
- W2183683505 created "2016-06-24" @default.
- W2183683505 creator A5091132899 @default.
- W2183683505 date "2014-01-01" @default.
- W2183683505 modified "2023-09-27" @default.
- W2183683505 title "Artificial neural network model of forecasting relative humidity in different humid and arid areas of China (EI)" @default.
- W2183683505 cites W2005289033 @default.
- W2183683505 cites W2007396336 @default.
- W2183683505 cites W2035174181 @default.
- W2183683505 cites W2036302056 @default.
- W2183683505 cites W2043269835 @default.
- W2183683505 cites W2048268983 @default.
- W2183683505 cites W2049313095 @default.
- W2183683505 cites W2070761092 @default.
- W2183683505 cites W2075491568 @default.
- W2183683505 cites W2094256310 @default.
- W2183683505 cites W2122298710 @default.
- W2183683505 cites W266074958 @default.
- W2183683505 cites W267255109 @default.
- W2183683505 hasPublicationYear "2014" @default.
- W2183683505 type Work @default.
- W2183683505 sameAs 2183683505 @default.
- W2183683505 citedByCount "0" @default.
- W2183683505 crossrefType "journal-article" @default.
- W2183683505 hasAuthorship W2183683505A5091132899 @default.
- W2183683505 hasConcept C105795698 @default.
- W2183683505 hasConcept C107054158 @default.
- W2183683505 hasConcept C119857082 @default.
- W2183683505 hasConcept C127313418 @default.
- W2183683505 hasConcept C150772632 @default.
- W2183683505 hasConcept C151420433 @default.
- W2183683505 hasConcept C151730666 @default.
- W2183683505 hasConcept C153294291 @default.
- W2183683505 hasConcept C158960510 @default.
- W2183683505 hasConcept C161067210 @default.
- W2183683505 hasConcept C205649164 @default.
- W2183683505 hasConcept C2780092901 @default.
- W2183683505 hasConcept C33923547 @default.
- W2183683505 hasConcept C39432304 @default.
- W2183683505 hasConcept C41008148 @default.
- W2183683505 hasConcept C49204034 @default.
- W2183683505 hasConcept C50644808 @default.
- W2183683505 hasConcept C86803240 @default.
- W2183683505 hasConcept C91586092 @default.
- W2183683505 hasConceptScore W2183683505C105795698 @default.
- W2183683505 hasConceptScore W2183683505C107054158 @default.
- W2183683505 hasConceptScore W2183683505C119857082 @default.
- W2183683505 hasConceptScore W2183683505C127313418 @default.
- W2183683505 hasConceptScore W2183683505C150772632 @default.
- W2183683505 hasConceptScore W2183683505C151420433 @default.
- W2183683505 hasConceptScore W2183683505C151730666 @default.
- W2183683505 hasConceptScore W2183683505C153294291 @default.
- W2183683505 hasConceptScore W2183683505C158960510 @default.
- W2183683505 hasConceptScore W2183683505C161067210 @default.
- W2183683505 hasConceptScore W2183683505C205649164 @default.
- W2183683505 hasConceptScore W2183683505C2780092901 @default.
- W2183683505 hasConceptScore W2183683505C33923547 @default.
- W2183683505 hasConceptScore W2183683505C39432304 @default.
- W2183683505 hasConceptScore W2183683505C41008148 @default.
- W2183683505 hasConceptScore W2183683505C49204034 @default.
- W2183683505 hasConceptScore W2183683505C50644808 @default.
- W2183683505 hasConceptScore W2183683505C86803240 @default.
- W2183683505 hasConceptScore W2183683505C91586092 @default.
- W2183683505 hasIssue "6" @default.
- W2183683505 hasLocation W21836835051 @default.
- W2183683505 hasOpenAccess W2183683505 @default.
- W2183683505 hasPrimaryLocation W21836835051 @default.
- W2183683505 hasRelatedWork W1464674155 @default.
- W2183683505 hasRelatedWork W2001554860 @default.
- W2183683505 hasRelatedWork W2014403600 @default.
- W2183683505 hasRelatedWork W2036665462 @default.
- W2183683505 hasRelatedWork W2038259980 @default.
- W2183683505 hasRelatedWork W2158546100 @default.
- W2183683505 hasRelatedWork W2185439529 @default.
- W2183683505 hasRelatedWork W2347818927 @default.
- W2183683505 hasRelatedWork W2360091476 @default.
- W2183683505 hasRelatedWork W2360198467 @default.
- W2183683505 hasRelatedWork W2372706763 @default.
- W2183683505 hasRelatedWork W2373024873 @default.
- W2183683505 hasRelatedWork W2384375680 @default.
- W2183683505 hasRelatedWork W2385393853 @default.
- W2183683505 hasRelatedWork W2394176707 @default.
- W2183683505 hasRelatedWork W2911322720 @default.
- W2183683505 hasRelatedWork W2965771385 @default.
- W2183683505 hasRelatedWork W2972536252 @default.
- W2183683505 hasRelatedWork W3007571099 @default.
- W2183683505 hasRelatedWork W67654439 @default.
- W2183683505 hasVolume "18" @default.
- W2183683505 isParatext "false" @default.
- W2183683505 isRetracted "false" @default.
- W2183683505 magId "2183683505" @default.
- W2183683505 workType "article" @default.