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dc.contributor.authorCrowell, Kevin Lee
dc.date.accessioned2014-03-04T16:20:41Z
dc.date.available2014-03-04T16:20:41Z
dc.date.issued2008-12
dc.identifier.othercrowell_kevin_l_200812_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/crowell_kevin_l_200812_ms
dc.identifier.urihttp://hdl.handle.net/10724/25157
dc.description.abstractPrecipitation, in meteorology, is defined as any product, liquid or solid, of atmospheric water vapor that is accumulated onto the earth’s surface. Water, and thus precipitation, has a major impact on our daily livelihood. As such, the uncertainty of both the future occurrence and amount of precipitation can have a negative impact on many sectors of our economy, especially agriculture. There is, therefore, a need to use innovative computer technologies such as artificial intelligence to improve the accuracy of precipitation predictions. Artificial neural networks have been shown to be useful as an aid for the prediction of weather variables. The goal of this study was to develop artificial neural network models for the purpose of predicting both the Probability of Precipitation and quantitative precipitation over a 24-hour period beginning and ending at midnight.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectArtificial Neural Networks
dc.subjectProbabilistic Neural Network
dc.subjectPrecipitation
dc.subjectProbability of Precipitation
dc.subjectQuantitative Precipitation
dc.subjectBrier Score
dc.titlePrecipitation prediction using artificial neural networks
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence Center
dc.description.majorArtificial Intelligence
dc.description.advisorGerrit Hoogenboom
dc.description.committeeGerrit Hoogenboom
dc.description.committeeJoel Paz
dc.description.committeeWalter D. Potter
dc.description.committeeRon McClendon
dc.description.committeeGerrit Hoogenboom


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