Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait

Reem S. Ettouney, Sabah Abdul-Wahab, Amal S. Elkilani

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper focuses on modelling of emission inventory, pollutant dispersion by the industrial source complex short term model (ISCST), and neural network analysis of air pollution in Kuwait. A novel neural network-based scheme is suggested and applied to site-specific short-and medium-term forecasting of ozone concentrations. Two feed forward artificial neural networks (ANN) are used to improve the performance of time series predictions. Results show that this forecasting technique represents a significant improvement over the conventional ANN approach.

Original languageEnglish
Pages (from-to)193-206
Number of pages14
JournalInternational Journal of Environmental Studies
Volume66
Issue number2
DOIs
Publication statusPublished - Apr 2009

Fingerprint

Kuwait
emission inventory
air pollution
Air pollution
neural network
artificial neural network
atmospheric pollution
Neural networks
network analysis
modeling
ozone
time series
Electric network analysis
prediction
pollutant
Ozone
Time series
performance
pollutant dispersion

Keywords

  • Air pollution
  • Emission inventory
  • ISCST and neural network modelling
  • Monitoring
  • Seasonal and temporal variations

ASJC Scopus subject areas

  • Waste Management and Disposal
  • Pollution
  • Computers in Earth Sciences
  • Geography, Planning and Development
  • Ecology

Cite this

Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait. / Ettouney, Reem S.; Abdul-Wahab, Sabah; Elkilani, Amal S.

In: International Journal of Environmental Studies, Vol. 66, No. 2, 04.2009, p. 193-206.

Research output: Contribution to journalArticle

@article{fe8648d2d3b142c39d60f40997cbac3c,
title = "Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait",
abstract = "This paper focuses on modelling of emission inventory, pollutant dispersion by the industrial source complex short term model (ISCST), and neural network analysis of air pollution in Kuwait. A novel neural network-based scheme is suggested and applied to site-specific short-and medium-term forecasting of ozone concentrations. Two feed forward artificial neural networks (ANN) are used to improve the performance of time series predictions. Results show that this forecasting technique represents a significant improvement over the conventional ANN approach.",
keywords = "Air pollution, Emission inventory, ISCST and neural network modelling, Monitoring, Seasonal and temporal variations",
author = "Ettouney, {Reem S.} and Sabah Abdul-Wahab and Elkilani, {Amal S.}",
year = "2009",
month = "4",
doi = "10.1080/00207230902859929",
language = "English",
volume = "66",
pages = "193--206",
journal = "International Journal of Environmental Studies",
issn = "0020-7233",
publisher = "Taylor and Francis Ltd.",
number = "2",

}

TY - JOUR

T1 - Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait

AU - Ettouney, Reem S.

AU - Abdul-Wahab, Sabah

AU - Elkilani, Amal S.

PY - 2009/4

Y1 - 2009/4

N2 - This paper focuses on modelling of emission inventory, pollutant dispersion by the industrial source complex short term model (ISCST), and neural network analysis of air pollution in Kuwait. A novel neural network-based scheme is suggested and applied to site-specific short-and medium-term forecasting of ozone concentrations. Two feed forward artificial neural networks (ANN) are used to improve the performance of time series predictions. Results show that this forecasting technique represents a significant improvement over the conventional ANN approach.

AB - This paper focuses on modelling of emission inventory, pollutant dispersion by the industrial source complex short term model (ISCST), and neural network analysis of air pollution in Kuwait. A novel neural network-based scheme is suggested and applied to site-specific short-and medium-term forecasting of ozone concentrations. Two feed forward artificial neural networks (ANN) are used to improve the performance of time series predictions. Results show that this forecasting technique represents a significant improvement over the conventional ANN approach.

KW - Air pollution

KW - Emission inventory

KW - ISCST and neural network modelling

KW - Monitoring

KW - Seasonal and temporal variations

UR - http://www.scopus.com/inward/record.url?scp=69149093076&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=69149093076&partnerID=8YFLogxK

U2 - 10.1080/00207230902859929

DO - 10.1080/00207230902859929

M3 - Article

VL - 66

SP - 193

EP - 206

JO - International Journal of Environmental Studies

JF - International Journal of Environmental Studies

SN - 0020-7233

IS - 2

ER -