Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM10 Concentrations and Emissions from Swine Buildings

dc.contributor.author Sun, Gang
dc.contributor.author Hoff, Steven
dc.contributor.author Hoff, Steven
dc.contributor.author Zelle, Brian
dc.contributor.author Smith, Minda
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-02-13T10:13:48.000
dc.date.accessioned 2020-06-29T22:33:18Z
dc.date.available 2020-06-29T22:33:18Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2008
dc.date.embargo 2013-04-30
dc.date.issued 2008-06-01
dc.description.abstract <p>The quantification of diurnal and seasonal gas (NH3, H2S, and CO2) and PM10 concentrations and emission rates (GPCER) from livestock production facilities is indispensable for the development of science-based setback determination methods and evaluation of improved downwind community air quality resulting from the implementation of gas pollution control. The purpose of this study was to employ backpropagation neural network (BPNN) and generalized regression neural network (GRNN) techniques to model GPCER generated and emitted from swine deep-pit finishing buildings as affected by time of day, season, ventilation rates, animal growth cycles, in-house manure storage levels, and weather conditions. The statistical results revealed that the BPNN and GRNN models were successfully developed to forecast hourly GPCER with very high coefficients of determination (R2) from 81.15% to 99.46% and very low values of systemic performance indexes. These good results indicated that the artificial neural network (ANN) technologies were capable of accurately modeling source air quality within and from the animal operations. It was also found that the process of constructing, training, and simulating the BPNN models was very complex. Some trial-and-error methods combined with a thorough understanding of theoretical backpropagation were required in order to obtain satisfying predictive results. The GRNN, based on nonlinear regression theory, can approximate any arbitrary function between input and output vectors and has a fast training time, great stability, and relatively easy network parameter settings during the training stage in comparison to the BPNN method. Thus, the GRNN was characterized as a preferred solution for its use in air quality modeling.</p>
dc.description.comments <p>This is an ASABE Meeting Presentation, Paper No. <a href="http://elibrary.asabe.org/abstract.asp?aid=25180&t=3&dabs=Y&redir=&redirType=" target="_blank">085100</a>.</p>
dc.identifier archive/lib.dr.iastate.edu/abe_eng_conf/305/
dc.identifier.articleid 1303
dc.identifier.contextkey 4089961
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_conf/305
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/323
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_conf/305/2008_SunG_DevelopmentComparisonBackpropagation.pdf|||Fri Jan 14 23:28:53 UTC 2022
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Backpropogation
dc.subject.keywords Diurnal
dc.subject.keywords Gas
dc.subject.keywords GRNN
dc.subject.keywords PM10
dc.subject.keywords Seasonal
dc.subject.keywords Swine buildings
dc.title Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM10 Concentrations and Emissions from Swine Buildings
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication 98b46d48-66a2-4458-9b42-8c4aa050664d
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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