Paper Info
Title
Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms.
Abstract
A genetic algorithm is paired with a Lagrangian puff atmospheric model to reconstruct the source characteristics of an atmospheric release. Observed meteorological and ground concentration measurements from the real-world Dipole Pride controlled release experiment are used to test the methodology. A sensitivity study is performed to quantify the relative contribution of the number and location of sensor measurements by progressively removing them. Additionally, the importance of the meteorological measurements is tested by progressively removing surface observations and vertical profiles. It is shown that the source term reconstruction can occur also with limited meteorological observations. The proposed general methodology can be applied to reconstruct the characteristics of an unknown atmospheric release given limited ground and meteorological observations.
Year
DOI
Venue
2017
10.1080/08839514.2017.1300005
Applied Artificial Intelligence
Field
DocType
Volume
Data mining,Source reconstruction,Lagrangian,Computer science,Remote sensing,Atmospheric model,Genetic algorithm,Dipole
Journal
31
Issue
ISSN
Citations 
2
0883-9514
0
PageRank 
References 
Authors
0.34
5
5
Name
Order
Citations
PageRank
Alessio Petrozziello1492.21
Guido Cervone24411.29
Pasquale Franzese332.14
Sue Ellen Haupt4367.63
R. Cerulli525223.85