SENSITIVITY ANALYSIS OF MACHINE LEARNING IN BRIGHTNESS TEMPERATURE PREDICTIONS OVER SNOW-COVERD REGIONS USING THE ADVANCED MICROWAVE SCANNING RADIOMETER

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dc.contributor.advisorForman, Bartonen_US
dc.contributor.authorXue, Yuanen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2014-06-26T05:40:28Z
dc.date.available2014-06-26T05:40:28Z
dc.date.issued2014en_US
dc.description.abstractSnow is a critical component in the global energy and hydrologic cycle. Further, it is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. Since direct quantification of snow water equivalent (SWE) is complicated by spatial and temporal variability, space-borne passive microwave SWE retrieval products have been utilized over regional and continental-scales to better estimate SWE. Previous studies have explored the possibility of employing machine learning, namely an artificial neural network (ANN) or a support vector machine (SVM), to replace the traditional radiative transfer model (RTM) during brightness temperatures (Tb) assimilation. However, we still need to address the following question: What are the most significant parameters in the machine-learning model based on either ANN or SVM? The goal of this study is to compare and contrast sensitivity analysis of Tb with respect to each model input between the ANN- and SVM-based estimates. In general, the results suggest the SVM (relative to the ANN) may be more beneficial during Tb assimilation studies where enhanced SWE estimation is the main objective.en_US
dc.identifier.urihttp://hdl.handle.net/1903/15475
dc.language.isoenen_US
dc.subject.pqcontrolledHydrologic sciencesen_US
dc.subject.pqcontrolledRemote sensingen_US
dc.subject.pquncontrolledbrightness temperature predictionsen_US
dc.subject.pquncontrolledmachine learningen_US
dc.subject.pquncontrolledpassive microwaveen_US
dc.subject.pquncontrolledsensitivity analysisen_US
dc.titleSENSITIVITY ANALYSIS OF MACHINE LEARNING IN BRIGHTNESS TEMPERATURE PREDICTIONS OVER SNOW-COVERD REGIONS USING THE ADVANCED MICROWAVE SCANNING RADIOMETERen_US
dc.typeThesisen_US

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