Transactions on Engineering and Computer Science
Statistical Analysis and Comparison of Optical Classification of Atmospheric Aerosol Lidar Data
Mohammed Alqawba, Norou Diawara, Kwasi G. Afrifa, Mohamed I. Elbakary, Mecit Cetin, and Khan M. Iftekharuddin
Abstract
In this article, we present 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 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 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 detailed statistical comparisons and analyses shows 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 efficient under appropriate functional distributions. The articleoffers a method for natural ordering of the aerosol types.
Keywords: Remote sensing and sensors; Lidar; Aerosol detection.
Journal Information
- Journal Name: Transactions on Engineering and Computer Science
- Journal Short Name: Trans Eng Comput Sci
- Language: English
- Format of Publication: Online
- Starting Year: 2020
- Focus Subject: Engineering and Computer Science
- Publication Model: Online
- Frequency of Publication: Two Issue a Year
- Review Process: Single blind peer-review by referees
- Time to 1st Decision: 2 to 3 weeks from date of submission
- Time to Acceptance: 4 to 6 weeks, depending upon the required revision cycles
- Time to Publication: 1 to 3 weeks from date of final submission in the forthcoming issue
- DOI Prefix: 10.7620
Indexed and Abstracted In
