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Analysis of short-term estuarine phytoplankton dynamics using neural networks
The artificial neural network (ANN) approach was investigated to model the short-term phytoplankton dynamics in the Skidaway River Estuary. The ability of ANN to model phytoplankton biomass and density of dominant species was evaluated using surface water sampling data collected during bloom and non...
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| creator | Ying Zhang Zhenhua Xie Parris, D. J. Cohen, R. A. |
| description | The artificial neural network (ANN) approach was investigated to model the short-term phytoplankton dynamics in the Skidaway River Estuary. The ability of ANN to model phytoplankton biomass and density of dominant species was evaluated using surface water sampling data collected during bloom and non-bloom periods. During the spring bloom period, the ANN models provided good accuracy for phytoplankton biomass and densities of rapidly growing species using salinity, nitrate, temperature and dissolved oxygen. During the non-bloom period, variation of phytoplankton was small and could not be modeled successfully using the same four environmental factors used to create spring bloom models. Lagged phytoplankton measurements can be added as inputs to increase accuracy of all phytoplankton models. |
| doi_str_mv | 10.1109/ICNC.2011.6022246 |
| format | conference_proceeding |
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J. ; Cohen, R. A.</creator><creatorcontrib>Ying Zhang ; Zhenhua Xie ; Parris, D. J. ; Cohen, R. A.</creatorcontrib><description>The artificial neural network (ANN) approach was investigated to model the short-term phytoplankton dynamics in the Skidaway River Estuary. The ability of ANN to model phytoplankton biomass and density of dominant species was evaluated using surface water sampling data collected during bloom and non-bloom periods. During the spring bloom period, the ANN models provided good accuracy for phytoplankton biomass and densities of rapidly growing species using salinity, nitrate, temperature and dissolved oxygen. During the non-bloom period, variation of phytoplankton was small and could not be modeled successfully using the same four environmental factors used to create spring bloom models. Lagged phytoplankton measurements can be added as inputs to increase accuracy of all phytoplankton models.</description><identifier>ISSN: 2157-9555</identifier><identifier>ISBN: 9781424499502</identifier><identifier>ISBN: 142449950X</identifier><identifier>EISBN: 9781424499526</identifier><identifier>EISBN: 1424499534</identifier><identifier>EISBN: 9781424499533</identifier><identifier>EISBN: 1424499526</identifier><identifier>DOI: 10.1109/ICNC.2011.6022246</identifier><language>eng</language><publisher>IEEE</publisher><subject>artificial neural network ; Artificial neural networks ; Biological system modeling ; Biomass ; community composition ; dominant species ; estuary ; Rivers ; Sea measurements ; Skidaway River Estuary ; Springs ; Temperature measurement</subject><ispartof>2011 Seventh International Conference on Natural Computation, 2011, Vol.2, p.734-738</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6022246$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54530,54895,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6022246$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ying Zhang</creatorcontrib><creatorcontrib>Zhenhua Xie</creatorcontrib><creatorcontrib>Parris, D. 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Lagged phytoplankton measurements can be added as inputs to increase accuracy of all phytoplankton models.</description><subject>artificial neural network</subject><subject>Artificial neural networks</subject><subject>Biological system modeling</subject><subject>Biomass</subject><subject>community composition</subject><subject>dominant species</subject><subject>estuary</subject><subject>Rivers</subject><subject>Sea measurements</subject><subject>Skidaway River Estuary</subject><subject>Springs</subject><subject>Temperature measurement</subject><issn>2157-9555</issn><isbn>9781424499502</isbn><isbn>142449950X</isbn><isbn>9781424499526</isbn><isbn>1424499534</isbn><isbn>9781424499533</isbn><isbn>1424499526</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVkL1OwzAYAI0AiarkARCLXyDlsx3H8VhF_FQqsHSvvjQ2NU2cyHaE8vZUogvT6ZYbjpAHBivGQD9t6o96xYGxVQmc86K8IplWFSt4UWgteXn9z4HfkAVnUuVaSnlHshi_AUAwpRToBXlfe-zm6CIdLI3HIaQ8mdBTE9OEwXlDx-OchrFDf0qDp-3ssXeHSKfo_Bf1ZgrYnZF-hnCK9-TWYhdNduGS7F6ed_Vbvv183dTrbe40pBwbVmBVAqIEoRuDplWFECBR2pbzih1UeXYlZMOtNoCiaQyTwlqlkbVcLMnjX9YZY_ZjcD2GeX_5IX4ByX5SyQ</recordid><startdate>201107</startdate><enddate>201107</enddate><creator>Ying Zhang</creator><creator>Zhenhua Xie</creator><creator>Parris, D. J.</creator><creator>Cohen, R. A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201107</creationdate><title>Analysis of short-term estuarine phytoplankton dynamics using neural networks</title><author>Ying Zhang ; Zhenhua Xie ; Parris, D. J. ; Cohen, R. A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-ab14a860aa5039beaed743305a5fd2281c76433735b2f9e0a3bbe153ff79a1d23</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>artificial neural network</topic><topic>Artificial neural networks</topic><topic>Biological system modeling</topic><topic>Biomass</topic><topic>community composition</topic><topic>dominant species</topic><topic>estuary</topic><topic>Rivers</topic><topic>Sea measurements</topic><topic>Skidaway River Estuary</topic><topic>Springs</topic><topic>Temperature measurement</topic><toplevel>online_resources</toplevel><creatorcontrib>Ying Zhang</creatorcontrib><creatorcontrib>Zhenhua Xie</creatorcontrib><creatorcontrib>Parris, D. J.</creatorcontrib><creatorcontrib>Cohen, R. A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ying Zhang</au><au>Zhenhua Xie</au><au>Parris, D. J.</au><au>Cohen, R. A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Analysis of short-term estuarine phytoplankton dynamics using neural networks</atitle><btitle>2011 Seventh International Conference on Natural Computation</btitle><stitle>ICNC</stitle><date>2011-07</date><risdate>2011</risdate><volume>2</volume><spage>734</spage><epage>738</epage><pages>734-738</pages><issn>2157-9555</issn><isbn>9781424499502</isbn><isbn>142449950X</isbn><eisbn>9781424499526</eisbn><eisbn>1424499534</eisbn><eisbn>9781424499533</eisbn><eisbn>1424499526</eisbn><abstract>The artificial neural network (ANN) approach was investigated to model the short-term phytoplankton dynamics in the Skidaway River Estuary. The ability of ANN to model phytoplankton biomass and density of dominant species was evaluated using surface water sampling data collected during bloom and non-bloom periods. During the spring bloom period, the ANN models provided good accuracy for phytoplankton biomass and densities of rapidly growing species using salinity, nitrate, temperature and dissolved oxygen. During the non-bloom period, variation of phytoplankton was small and could not be modeled successfully using the same four environmental factors used to create spring bloom models. Lagged phytoplankton measurements can be added as inputs to increase accuracy of all phytoplankton models.</abstract><pub>IEEE</pub><doi>10.1109/ICNC.2011.6022246</doi><tpages>5</tpages></addata></record> |
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| identifier | ISSN: 2157-9555 |
| ispartof | 2011 Seventh International Conference on Natural Computation, 2011, Vol.2, p.734-738 |
| issn | 2157-9555 |
| language | eng |
| recordid | cdi_ieee_primary_6022246 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | artificial neural network Artificial neural networks Biological system modeling Biomass community composition dominant species estuary Rivers Sea measurements Skidaway River Estuary Springs Temperature measurement |
| title | Analysis of short-term estuarine phytoplankton dynamics using neural networks |
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