Monitoring point optimization in lake waters
In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitor...
Gespeichert in:
| Veröffentlicht in: | Water science & technology. Water supply 2020-09, Vol.20 (6), p.2348-2358 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2358 |
|---|---|
| container_issue | 6 |
| container_start_page | 2348 |
| container_title | Water science & technology. Water supply |
| container_volume | 20 |
| creator | Liu, Gaoxuan Ai, Jiaoyan Xu, Jun Zheng, Jianwu Yao, Dongyi |
| description | In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitoring points. The single-objective and multi-objective optimization of water quality parameters were tested respectively and used to make the fitting distribution map. The results show that the genetic neural network has obvious advantages over the traditional isometric monitoring in the distribution error of water quality parameters, and the data reflected by the results are still accurate and effective at least six weeks after optimization. The results show that a genetic neural network can significantly improve the efficiency of water quality monitoring. |
| doi_str_mv | 10.2166/ws.2020.147 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_webof</sourceid><recordid>TN_cdi_webofscience_primary_000617138500030CitationCount</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2481220170</sourcerecordid><originalsourceid>FETCH-LOGICAL-c298t-6351ea289297beddfd170e192ce5cbb2fb75c1f5d57ff5a9e7edfd9c1510d9d83</originalsourceid><addsrcrecordid>eNqNkDFPwzAUhC0EEqUw8QciMULKe04dxyOKKCAVscAcJY6NXFo72K4i-PWYFsHK9G747k7vCDlHmFEsy-sxzChQmOGcH5AJlsBz4KI63OkyF3wujslJCCsAyjnSCbl6dNZE5419zQZnbMzcEM3GfLbROJsZm63bN5WNbVQ-nJIj3a6DOvu5U_KyuH2u7_Pl091DfbPMJRVVzMuCoWppJajgnep73SMHhYJKxWTXUd1xJlGznnGtWSsUV4kREhlCL_qqmJKLfe7g3ftWhdis3NbbVNnQeYWUQgpM1OWekt6F4JVuBm82rf9oEJrvOZox8WmOJs2R6GpPj6pzOkijrFS_DgAokWNRsaQKqE3c_V-7rY1_Rf-xFl_HV3Nl</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2481220170</pqid></control><display><type>article</type><title>Monitoring point optimization in lake waters</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><creator>Liu, Gaoxuan ; Ai, Jiaoyan ; Xu, Jun ; Zheng, Jianwu ; Yao, Dongyi</creator><creatorcontrib>Liu, Gaoxuan ; Ai, Jiaoyan ; Xu, Jun ; Zheng, Jianwu ; Yao, Dongyi</creatorcontrib><description>In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitoring points. The single-objective and multi-objective optimization of water quality parameters were tested respectively and used to make the fitting distribution map. The results show that the genetic neural network has obvious advantages over the traditional isometric monitoring in the distribution error of water quality parameters, and the data reflected by the results are still accurate and effective at least six weeks after optimization. The results show that a genetic neural network can significantly improve the efficiency of water quality monitoring.</description><identifier>ISSN: 1606-9749</identifier><identifier>EISSN: 1607-0798</identifier><identifier>DOI: 10.2166/ws.2020.147</identifier><language>eng</language><publisher>LONDON: Iwa Publishing</publisher><subject>Chlorophyll ; Cluster analysis ; Distribution ; Engineering ; Engineering, Environmental ; Environmental monitoring ; Environmental Sciences ; Environmental Sciences & Ecology ; Experiments ; Genetic algorithms ; Geography ; Isometric ; Lakes ; Life Sciences & Biomedicine ; Mean square errors ; Monitoring systems ; Multiple objective analysis ; Neural networks ; Optimization ; Parameters ; Physical Sciences ; Radial basis function ; Science & Technology ; Technology ; Unmanned vehicles ; Water monitoring ; Water quality ; Water quality management ; Water Resources ; Water temperature</subject><ispartof>Water science & technology. Water supply, 2020-09, Vol.20 (6), p.2348-2358</ispartof><rights>Copyright IWA Publishing Sep 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>1</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000617138500030</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c298t-6351ea289297beddfd170e192ce5cbb2fb75c1f5d57ff5a9e7edfd9c1510d9d83</citedby><cites>FETCH-LOGICAL-c298t-6351ea289297beddfd170e192ce5cbb2fb75c1f5d57ff5a9e7edfd9c1510d9d83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,782,786,27931,27932,28255</link.rule.ids></links><search><creatorcontrib>Liu, Gaoxuan</creatorcontrib><creatorcontrib>Ai, Jiaoyan</creatorcontrib><creatorcontrib>Xu, Jun</creatorcontrib><creatorcontrib>Zheng, Jianwu</creatorcontrib><creatorcontrib>Yao, Dongyi</creatorcontrib><title>Monitoring point optimization in lake waters</title><title>Water science & technology. Water supply</title><addtitle>WATER SUPPLY</addtitle><description>In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitoring points. The single-objective and multi-objective optimization of water quality parameters were tested respectively and used to make the fitting distribution map. The results show that the genetic neural network has obvious advantages over the traditional isometric monitoring in the distribution error of water quality parameters, and the data reflected by the results are still accurate and effective at least six weeks after optimization. The results show that a genetic neural network can significantly improve the efficiency of water quality monitoring.</description><subject>Chlorophyll</subject><subject>Cluster analysis</subject><subject>Distribution</subject><subject>Engineering</subject><subject>Engineering, Environmental</subject><subject>Environmental monitoring</subject><subject>Environmental Sciences</subject><subject>Environmental Sciences & Ecology</subject><subject>Experiments</subject><subject>Genetic algorithms</subject><subject>Geography</subject><subject>Isometric</subject><subject>Lakes</subject><subject>Life Sciences & Biomedicine</subject><subject>Mean square errors</subject><subject>Monitoring systems</subject><subject>Multiple objective analysis</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Parameters</subject><subject>Physical Sciences</subject><subject>Radial basis function</subject><subject>Science & Technology</subject><subject>Technology</subject><subject>Unmanned vehicles</subject><subject>Water monitoring</subject><subject>Water quality</subject><subject>Water quality management</subject><subject>Water Resources</subject><subject>Water temperature</subject><issn>1606-9749</issn><issn>1607-0798</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNkDFPwzAUhC0EEqUw8QciMULKe04dxyOKKCAVscAcJY6NXFo72K4i-PWYFsHK9G747k7vCDlHmFEsy-sxzChQmOGcH5AJlsBz4KI63OkyF3wujslJCCsAyjnSCbl6dNZE5419zQZnbMzcEM3GfLbROJsZm63bN5WNbVQ-nJIj3a6DOvu5U_KyuH2u7_Pl091DfbPMJRVVzMuCoWppJajgnep73SMHhYJKxWTXUd1xJlGznnGtWSsUV4kREhlCL_qqmJKLfe7g3ftWhdis3NbbVNnQeYWUQgpM1OWekt6F4JVuBm82rf9oEJrvOZox8WmOJs2R6GpPj6pzOkijrFS_DgAokWNRsaQKqE3c_V-7rY1_Rf-xFl_HV3Nl</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Liu, Gaoxuan</creator><creator>Ai, Jiaoyan</creator><creator>Xu, Jun</creator><creator>Zheng, Jianwu</creator><creator>Yao, Dongyi</creator><general>Iwa Publishing</general><general>IWA Publishing</general><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7UA</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>H96</scope><scope>H97</scope><scope>HCIFZ</scope><scope>L.G</scope><scope>L6V</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20200901</creationdate><title>Monitoring point optimization in lake waters</title><author>Liu, Gaoxuan ; Ai, Jiaoyan ; Xu, Jun ; Zheng, Jianwu ; Yao, Dongyi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c298t-6351ea289297beddfd170e192ce5cbb2fb75c1f5d57ff5a9e7edfd9c1510d9d83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Chlorophyll</topic><topic>Cluster analysis</topic><topic>Distribution</topic><topic>Engineering</topic><topic>Engineering, Environmental</topic><topic>Environmental monitoring</topic><topic>Environmental Sciences</topic><topic>Environmental Sciences & Ecology</topic><topic>Experiments</topic><topic>Genetic algorithms</topic><topic>Geography</topic><topic>Isometric</topic><topic>Lakes</topic><topic>Life Sciences & Biomedicine</topic><topic>Mean square errors</topic><topic>Monitoring systems</topic><topic>Multiple objective analysis</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Parameters</topic><topic>Physical Sciences</topic><topic>Radial basis function</topic><topic>Science & Technology</topic><topic>Technology</topic><topic>Unmanned vehicles</topic><topic>Water monitoring</topic><topic>Water quality</topic><topic>Water quality management</topic><topic>Water Resources</topic><topic>Water temperature</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Gaoxuan</creatorcontrib><creatorcontrib>Ai, Jiaoyan</creatorcontrib><creatorcontrib>Xu, Jun</creatorcontrib><creatorcontrib>Zheng, Jianwu</creatorcontrib><creatorcontrib>Yao, Dongyi</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><jtitle>Water science & technology. Water supply</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Gaoxuan</au><au>Ai, Jiaoyan</au><au>Xu, Jun</au><au>Zheng, Jianwu</au><au>Yao, Dongyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monitoring point optimization in lake waters</atitle><jtitle>Water science & technology. Water supply</jtitle><stitle>WATER SUPPLY</stitle><date>2020-09-01</date><risdate>2020</risdate><volume>20</volume><issue>6</issue><spage>2348</spage><epage>2358</epage><pages>2348-2358</pages><issn>1606-9749</issn><eissn>1607-0798</eissn><abstract>In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitoring points. The single-objective and multi-objective optimization of water quality parameters were tested respectively and used to make the fitting distribution map. The results show that the genetic neural network has obvious advantages over the traditional isometric monitoring in the distribution error of water quality parameters, and the data reflected by the results are still accurate and effective at least six weeks after optimization. The results show that a genetic neural network can significantly improve the efficiency of water quality monitoring.</abstract><cop>LONDON</cop><pub>Iwa Publishing</pub><doi>10.2166/ws.2020.147</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1606-9749 |
| ispartof | Water science & technology. Water supply, 2020-09, Vol.20 (6), p.2348-2358 |
| issn | 1606-9749 1607-0798 |
| language | eng |
| recordid | cdi_webofscience_primary_000617138500030CitationCount |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /> |
| subjects | Chlorophyll Cluster analysis Distribution Engineering Engineering, Environmental Environmental monitoring Environmental Sciences Environmental Sciences & Ecology Experiments Genetic algorithms Geography Isometric Lakes Life Sciences & Biomedicine Mean square errors Monitoring systems Multiple objective analysis Neural networks Optimization Parameters Physical Sciences Radial basis function Science & Technology Technology Unmanned vehicles Water monitoring Water quality Water quality management Water Resources Water temperature |
| title | Monitoring point optimization in lake waters |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-04T23%3A32%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_webof&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Monitoring%20point%20optimization%20in%20lake%20waters&rft.jtitle=Water%20science%20&%20technology.%20Water%20supply&rft.au=Liu,%20Gaoxuan&rft.date=2020-09-01&rft.volume=20&rft.issue=6&rft.spage=2348&rft.epage=2358&rft.pages=2348-2358&rft.issn=1606-9749&rft.eissn=1607-0798&rft_id=info:doi/10.2166/ws.2020.147&rft_dat=%3Cproquest_webof%3E2481220170%3C/proquest_webof%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2481220170&rft_id=info:pmid/&rfr_iscdi=true |