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

2 Scopus citations

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

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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