Cargando…

Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model

Models to predict seasonal hydrogen sulfide (H(2)S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H(2)S were collected from August 2005 to Fal...

Descripción completa

Detalles Bibliográficos
Autores principales: Kim, Byungwhan, Lee, Joogong, Jang, Jungyoung, Han, Dongil, Kim, Ki-Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: TheScientificWorldJOURNAL 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5720030/
https://www.ncbi.nlm.nih.gov/pubmed/21552763
http://dx.doi.org/10.1100/tsw.2011.95
_version_ 1783284603167965184
author Kim, Byungwhan
Lee, Joogong
Jang, Jungyoung
Han, Dongil
Kim, Ki-Hyun
author_facet Kim, Byungwhan
Lee, Joogong
Jang, Jungyoung
Han, Dongil
Kim, Ki-Hyun
author_sort Kim, Byungwhan
collection PubMed
description Models to predict seasonal hydrogen sulfide (H(2)S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H(2)S were collected from August 2005 to Fall 2006 from a huge industrial complex located in Ansan City, Korea. Three types of seasonal groupings were prepared and one optimized model is built for each dataset. These optimized models were then used for the analysis of the sensitivity and main effect of the parameters. H(2)S was noted to be very sensitive to rainfall during the spring and summer. In the autumn, its sensitivity showed a strong dependency on wind speed and pressure. Pressure was identified as the most influential parameter during the spring and summer. In the autumn, relative humidity overwhelmingly affected H(2)S. It was noted that H(2)S maintained an inverse relationship with a number of parameters (e.g., radiation, wind speed, or dew-point temperature). In contrast, it exhibited a declining trend with a decrease in pressure. An increase in radiation was likely to decrease during spring and summer, but the opposite trend was predicted for the autumn. The overall results of this study thus suggest that the behavior of H(2)S can be accounted for by a diverse combination of meteorological parameters across seasons.
format Online
Article
Text
id pubmed-5720030
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher TheScientificWorldJOURNAL
record_format MEDLINE/PubMed
spelling pubmed-57200302017-12-21 Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model Kim, Byungwhan Lee, Joogong Jang, Jungyoung Han, Dongil Kim, Ki-Hyun ScientificWorldJournal Research Article Models to predict seasonal hydrogen sulfide (H(2)S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H(2)S were collected from August 2005 to Fall 2006 from a huge industrial complex located in Ansan City, Korea. Three types of seasonal groupings were prepared and one optimized model is built for each dataset. These optimized models were then used for the analysis of the sensitivity and main effect of the parameters. H(2)S was noted to be very sensitive to rainfall during the spring and summer. In the autumn, its sensitivity showed a strong dependency on wind speed and pressure. Pressure was identified as the most influential parameter during the spring and summer. In the autumn, relative humidity overwhelmingly affected H(2)S. It was noted that H(2)S maintained an inverse relationship with a number of parameters (e.g., radiation, wind speed, or dew-point temperature). In contrast, it exhibited a declining trend with a decrease in pressure. An increase in radiation was likely to decrease during spring and summer, but the opposite trend was predicted for the autumn. The overall results of this study thus suggest that the behavior of H(2)S can be accounted for by a diverse combination of meteorological parameters across seasons. TheScientificWorldJOURNAL 2011-05-05 /pmc/articles/PMC5720030/ /pubmed/21552763 http://dx.doi.org/10.1100/tsw.2011.95 Text en Copyright © 2011 Byungwhan Kim et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Kim, Byungwhan
Lee, Joogong
Jang, Jungyoung
Han, Dongil
Kim, Ki-Hyun
Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model
title Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model
title_full Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model
title_fullStr Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model
title_full_unstemmed Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model
title_short Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model
title_sort prediction on the seasonal behavior of hydrogen sulfide using a neural network model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5720030/
https://www.ncbi.nlm.nih.gov/pubmed/21552763
http://dx.doi.org/10.1100/tsw.2011.95
work_keys_str_mv AT kimbyungwhan predictionontheseasonalbehaviorofhydrogensulfideusinganeuralnetworkmodel
AT leejoogong predictionontheseasonalbehaviorofhydrogensulfideusinganeuralnetworkmodel
AT jangjungyoung predictionontheseasonalbehaviorofhydrogensulfideusinganeuralnetworkmodel
AT handongil predictionontheseasonalbehaviorofhydrogensulfideusinganeuralnetworkmodel
AT kimkihyun predictionontheseasonalbehaviorofhydrogensulfideusinganeuralnetworkmodel