Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms

Alessio Petrozziello, Guido Cervone, Pasquale Franzese, Sue Ellen Haupt, Raffaele Cerulli

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)119-133
Number of pages15
JournalApplied Artificial Intelligence
Volume31
Issue number2
DOIs
StatePublished - Feb 7 2017

Fingerprint

Genetic algorithms
Sensors
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Petrozziello, Alessio ; Cervone, Guido ; Franzese, Pasquale ; Haupt, Sue Ellen ; Cerulli, Raffaele. / Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms. In: Applied Artificial Intelligence. 2017 ; Vol. 31, No. 2. pp. 119-133.
@article{020dab82a9e04ad1853ca071d1616937,
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.",
author = "Alessio Petrozziello and Guido Cervone and Pasquale Franzese and Haupt, {Sue Ellen} and Raffaele Cerulli",
year = "2017",
month = "2",
day = "7",
doi = "10.1080/08839514.2017.1300005",
language = "English (US)",
volume = "31",
pages = "119--133",
journal = "Applied Artificial Intelligence",
issn = "0883-9514",
publisher = "Taylor and Francis Ltd.",
number = "2",

}

Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms. / Petrozziello, Alessio; Cervone, Guido; Franzese, Pasquale; Haupt, Sue Ellen; Cerulli, Raffaele.

In: Applied Artificial Intelligence, Vol. 31, No. 2, 07.02.2017, p. 119-133.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms

AU - Petrozziello, Alessio

AU - Cervone, Guido

AU - Franzese, Pasquale

AU - Haupt, Sue Ellen

AU - Cerulli, Raffaele

PY - 2017/2/7

Y1 - 2017/2/7

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85017438642&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85017438642&partnerID=8YFLogxK

U2 - 10.1080/08839514.2017.1300005

DO - 10.1080/08839514.2017.1300005

M3 - Article

AN - SCOPUS:85017438642

VL - 31

SP - 119

EP - 133

JO - Applied Artificial Intelligence

JF - Applied Artificial Intelligence

SN - 0883-9514

IS - 2

ER -