| dc.description | In this work, our intention is to develop ways to correlate and classify several types of aerosols, by
practical and objective manners, with the aim of machine learning techniques (specially decision
trees and random forests) [1, 2]. For this purpose, we are intended to use the AERONET
(Aerosol Robotic Network) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations) satellite databases [3]. The AERONET database, which includes measurements
made since year 2000, will provide to us a reference standard for the categorization and classification
of aerosols present in atmosphere [3]. Following this, the databases for the measurements made
by the CALIPSO satellite will be addressed, also with the objective of categorizing and classifying
aerosols. Such data mining processes will enable us to carry out statistical and climatological
analyzes of these databases, allowing a better study of the atmospheric behavior of aerosols
in the Earth???s atmosphere [4]. We believe that the development of such tools and techniques
for treatment of data provided by AERONET and CALIPSO will contribute greatly to a better
understanding of climate change processes on Earth, a subject of scientific interest, especially in
recent years. | |
