Date of Award

5-2018

Document Type

Doctoral Project

Degree Name

Doctor of Engineering (D Eng)

Department

Electrical/Computer Engineering

Committee Director

Khan M. Iftekharuddin

Committee Member

Xin Chunsheng

Committee Member

Norou Diawara

Abstract

This dissertation presents a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and also consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear models (GLM) and regression tree techniques are used to further analyze the performance of the LIDAR parameter-based aerosol classification methods. The goal of this part of GLM and regression tree analyses is to compare and contrast distinct classification data schemes, and compare the results with the measured aerosol reflection data in the atmosphere. The detail statistical comparison and analysis show that the optical methods adopted in this study for classification and prediction of various harmful aerosol types such as soot, carbon monoxide (CO), sulfates (SOx) and nitrates (NOx) are effective.

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