UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Ahmad, JawairiaSubject: search.filters.subject.Machine Learning

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    SENSITIVITY ANALYSIS OF SUPPORT VECTOR MACHINE PREDICTIONS OF PASSIVE MICROWAVE BRIGHTNESS TEMPERATURES OVER SNOW-COVERED TERRAIN IN HIGH MOUNTAIN ASIA
    (2018) Ahmad, Jawairia; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Spatial and temporal variation of snow in High Mountain Asia is very critical as it determines contribution of snowmelt to the freshwater supply of over 136 million people. Support vector machine (SVM) prediction of passive microwave brightness temperature spectral difference (ΔTb) as a function of NASA Land Information System (LIS) modeled geophysical states is investigated through a sensitivity analysis. AMSRE ΔTb measurements over snow-covered areas in the Indus basin are used for training the SVMs. Sensitivity analysis results conform with the known first-order physics. LIS input states that are directly linked to physical temperature demonstrate relatively higher sensitivity. Accuracy of LIS modeled states is further assessed through a comparative analysis between LIS derived and Advanced Scatterometer based Freeze/Melt/Thaw categorical datasets. Highest agreement of 22%, between the two datasets, is observed for freeze state. Analyses results provide insight into LIS’s land surface modeling ability over the Indus Basin.