+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Prediction of tropospheric ozone concentration by employing artificial neural networks

Prediction of tropospheric ozone concentration by employing artificial neural networks

Environmental Engineering Science: 9, 1249-1254

Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently various algorithms such as artificial neural networks (ANNs) is applied to air quality modeling. The present work aims to predict tropospheric ozone concentration by the ANN with three pollutant parameters and eight meteorological factors in selected areas.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 032908164

Download citation: RISBibTeXText

DOI: 10.1089/ees.2007.0183

Related references

Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmospheric Environment 98: 98-104, 2014

Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration. Atmospheric Environment 40(32): 6173-6180, 2006

Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks. Environmental Pollution 244: 288-294, 2019

The prediction of coronary atherosclerosis employing artificial neural networks. Clinical Cardiology 23(6): 453-456, 2000

Application of artificial neural networks on the prediction of surface ozone concentrations. Huan Jing Ke Xue= Huanjing Kexue 32(8): 2231-2235, 2011

Application of artificial neural networks to modeling and prediction of ambient ozone concentrations. Journal of the Air and Waste Management Association 50(5): 894-901, 2000

Prediction of Zn concentration in human seminal plasma of Normospermia samples by Artificial Neural Networks (ANN). Journal of Assisted Reproduction and Genetics 30(4): 453-459, 2013

Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy. Journal of Pharmacy and Pharmaceutical Sciences 18(5): 856-862, 2015

Prediction of sulfur dioxide concentration levels from the Mina Al-Fahal refinery in Oman using artificial neural networks. American Journal Of Environmental Sciences: 5, 473-481, 2008

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks. Analytica Chimica Acta 594(1): 107-118, 2007

Online prediction of onsets of seizure-like events in hippocampal neural networks using wavelet artificial neural networks. Annals of Biomedical Engineering 34(2): 282-294, 2006

A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction. Environmental Earth Sciences 71(7): 3147-3160, 2014

A mechanistic model for the prediction of the effects of rising tropospheric ozone concentration on wheat photosynthesis. Photosynthesis: from light to biosphere Volume V Proceedings of the Xth International Photosynthesis Congress, Montpellier, France, 20-25 August 1995: 829-832, 1995

Evaluation of artificial neural networks and kriging for the prediction of arsenic in Alaskan bedrock-derived stream sediments using gold concentration data. International Journal of Mining, Reclamation and Environment 21(4): 282-294, 2007

Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Current Topics in Medicinal Chemistry 8(18): 1580-1605, 2008