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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
TheScientificWorldJOURNAL
2011
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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 |
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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 |
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