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" Feature Selection of PERSIANN, based on Multiple Regression Analysis with Principal Component Analysis and Using Three-Cornered Hat method to evaluate Precipitation products "
Ata Akbari Asanjan
Sorooshian, Soroosh; Gao, Xiaogang
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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803995
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| Doc. No
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TL48807
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| Call number
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1803233753; 10117022
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| Main Entry
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Binothman, Albara M.
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| Title & Author
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Feature Selection of PERSIANN, based on Multiple Regression Analysis with Principal Component Analysis and Using Three-Cornered Hat method to evaluate Precipitation products\ Ata Akbari AsanjanSorooshian, Soroosh; Gao, Xiaogang
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| College
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University of California, Irvine
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| Date
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2016
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| Degree
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M.S.
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| field of study
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Civil Engineering
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| student score
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2016
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| Page No
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34
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| Note
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Committee members: Hsu, Kuo-Lin
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| Note
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Place of publication: United States, Ann Arbor; ISBN=978-1-339-78425-0
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| Abstract
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My thesis addresses two aspects of satellite precipitation estimation. In the first chapter, feature selection aspect of PERSIANN algorithm will be discussed. In the second chapter, the Generalized Three-Cornered Hat method is used for intercomparison of PERSIANN-CDR and TRMM and CRU datasets over Iran. For this part, a part of author’s collaboration with Professor Katiraie of Azad University, Tehran (Corresponding author: Katiraie-Boroujerdy) will be represented. Chapter three presents the summary and conclusions. The PERSIANN model is an Artificial Neural Network-based (ANN) model for precipitation estimation using satellite information, and the datasets generated by it have gained popularity for application in both weather and climate studies. Research related to the PERSIANN system is ongoing, and it mainly focuses on improving its accuracy required for various applications. One of these improvements in the system includes the input feature selection of the model which can help the Neural Network to better learn the precipitation pattern by adding more relevant information. The Multiple Regression Analysis (MRA), by taking the advantage of Principal Component Analysis (PCA) to solve the collinearity is employed as the framework for ranking those features or inputs that are most useful for the learning process.
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| Subject
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Civil engineering; Water Resource Management
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| Descriptor
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Applied sciences;Earth sciences;Feature selection;Multiple regression analysis;Percipitation;Persiann;Principal component analysis;Three-cornered hat
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| Added Entry
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Sorooshian, Soroosh; Gao, Xiaogang
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| Added Entry
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Civil EngineeringUniversity of California, Irvine
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