A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik
Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentr...
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| Veröffentlicht in: | Environmental monitoring and assessment 2024-06, Vol.196 (6), p.586-586, Article 586 |
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| creator | Mert, Berna Kırıl Kasapoğulları, Deniz |
| description | Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentrations in Iznik Lake, which is an important water source for the surrounding communities, characterization data were taken from five different water sources flowing into the lake between 2015 and 2021. These characterization results were evaluated with IBM SPSS Statistics 23 software, with the addition of the lake water quality system. For this purpose, seven distinct physicochemical parameters were measured and monitored in Karasu, Kırandere, Olukdere and Sölöz water sources flowing into the lake, to serve as input data. Concentration levels of 15 distinct heavy metals in Karsak Stream originating from the lake were as the output. Specifically, Sn for Karasu (0.999), Sb for Kırandere (1.000), Cr for Olukdere (1.000) and Pb and Se for Sölöz (0.995) indicate parameter estimation
R
2
coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution. |
| doi_str_mv | 10.1007/s10661-024-12730-y |
| format | Article |
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R
2
coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution.</description><identifier>ISSN: 0167-6369</identifier><identifier>ISSN: 1573-2959</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-024-12730-y</identifier><identifier>PMID: 38809274</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Antimony ; Artificial neural networks ; Atmospheric Protection/Air Quality Control/Air Pollution ; case studies ; Chromium ; computer software ; Earth and Environmental Science ; Ecology ; ecosystems ; Ecotoxicology ; Environment ; Environmental Management ; Environmental Monitoring ; Heavy metals ; Independent variables ; Lake water ; Lake water quality ; Lakes ; Lakes - chemistry ; Lead ; Metal concentrations ; Metals, Heavy - analysis ; Monitoring/Environmental Analysis ; Neural networks ; Neural Networks, Computer ; Parameter estimation ; Parameters ; Physicochemical processes ; Physicochemical properties ; Pollution ; prediction ; Selenium ; statistics ; Stream pollution ; streams ; Sustainable ecosystems ; temperature ; Tin ; Turkey ; Water budget ; Water Pollutants, Chemical - analysis ; Water Pollution, Chemical - statistics & numerical data ; Water Quality ; Water resources ; Water sources</subject><ispartof>Environmental monitoring and assessment, 2024-06, Vol.196 (6), p.586-586, Article 586</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c403t-d870b72cf58a66bf7a7d5f9301f2db8d68814d296eef86197735cd2eb4c490b03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10661-024-12730-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-024-12730-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27907,27908,41471,42540,51302</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38809274$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mert, Berna Kırıl</creatorcontrib><creatorcontrib>Kasapoğulları, Deniz</creatorcontrib><title>A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><addtitle>Environ Monit Assess</addtitle><description>Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentrations in Iznik Lake, which is an important water source for the surrounding communities, characterization data were taken from five different water sources flowing into the lake between 2015 and 2021. These characterization results were evaluated with IBM SPSS Statistics 23 software, with the addition of the lake water quality system. For this purpose, seven distinct physicochemical parameters were measured and monitored in Karasu, Kırandere, Olukdere and Sölöz water sources flowing into the lake, to serve as input data. Concentration levels of 15 distinct heavy metals in Karsak Stream originating from the lake were as the output. Specifically, Sn for Karasu (0.999), Sb for Kırandere (1.000), Cr for Olukdere (1.000) and Pb and Se for Sölöz (0.995) indicate parameter estimation
R
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coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution.</description><subject>Antimony</subject><subject>Artificial neural networks</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>case studies</subject><subject>Chromium</subject><subject>computer software</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>ecosystems</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Environmental Monitoring</subject><subject>Heavy metals</subject><subject>Independent variables</subject><subject>Lake water</subject><subject>Lake water quality</subject><subject>Lakes</subject><subject>Lakes - chemistry</subject><subject>Lead</subject><subject>Metal concentrations</subject><subject>Metals, Heavy - analysis</subject><subject>Monitoring/Environmental Analysis</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Parameter estimation</subject><subject>Parameters</subject><subject>Physicochemical processes</subject><subject>Physicochemical properties</subject><subject>Pollution</subject><subject>prediction</subject><subject>Selenium</subject><subject>statistics</subject><subject>Stream pollution</subject><subject>streams</subject><subject>Sustainable ecosystems</subject><subject>temperature</subject><subject>Tin</subject><subject>Turkey</subject><subject>Water budget</subject><subject>Water Pollutants, Chemical - analysis</subject><subject>Water Pollution, Chemical - statistics & numerical data</subject><subject>Water Quality</subject><subject>Water resources</subject><subject>Water sources</subject><issn>0167-6369</issn><issn>1573-2959</issn><issn>1573-2959</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkT1vFDEQhi0EIpfAH6BAlmjSLIztXX-UUQRJpJNooLa8_gjO7a0vtjdo-fXZ3CUgpQjVFPO872j0IPSBwGcCIL4UApyTBmjbECoYNPMrtCKdYA1VnXqNVkC4aDjj6ggdl3IDAEq06i06YlKCoqJdIX2GrSkelzq5GaeApxLHa2xyjSHaaAY8-invR_2d8qbgmvAuexdtxb-8uZvx1tdlv0vDMNWYRhxHvDYbj6_-jHHzDr0JZij-_eM8QT-_ff1xftmsv19cnZ-tG9sCq42TAnpBbeik4bwPwgjXBcWABOp66biUpHVUce-D5EQJwTrrqO9b2yrogZ2g00PvLqfbyZeqt7FYPwxm9GkqmpGOcS4k6_6PAidCthQe0E_P0Js05XF5ZE91oBQjC0UPlM2plOyD3uW4NXnWBPSDKX0wpRdTem9Kz0vo42P11G-9-xt5UrMA7ACUZTVe-_zv9gu192tnnjU</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Mert, Berna Kırıl</creator><creator>Kasapoğulları, Deniz</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7TG</scope><scope>7TN</scope><scope>7U7</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H97</scope><scope>K9.</scope><scope>KL.</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20240601</creationdate><title>A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik</title><author>Mert, Berna Kırıl ; Kasapoğulları, Deniz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c403t-d870b72cf58a66bf7a7d5f9301f2db8d68814d296eef86197735cd2eb4c490b03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Antimony</topic><topic>Artificial neural networks</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>case studies</topic><topic>Chromium</topic><topic>computer software</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>ecosystems</topic><topic>Ecotoxicology</topic><topic>Environment</topic><topic>Environmental Management</topic><topic>Environmental Monitoring</topic><topic>Heavy metals</topic><topic>Independent variables</topic><topic>Lake water</topic><topic>Lake water quality</topic><topic>Lakes</topic><topic>Lakes - chemistry</topic><topic>Lead</topic><topic>Metal concentrations</topic><topic>Metals, Heavy - analysis</topic><topic>Monitoring/Environmental Analysis</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Parameter estimation</topic><topic>Parameters</topic><topic>Physicochemical processes</topic><topic>Physicochemical properties</topic><topic>Pollution</topic><topic>prediction</topic><topic>Selenium</topic><topic>statistics</topic><topic>Stream pollution</topic><topic>streams</topic><topic>Sustainable ecosystems</topic><topic>temperature</topic><topic>Tin</topic><topic>Turkey</topic><topic>Water budget</topic><topic>Water Pollutants, Chemical - analysis</topic><topic>Water Pollution, Chemical - statistics & numerical data</topic><topic>Water Quality</topic><topic>Water resources</topic><topic>Water sources</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mert, Berna Kırıl</creatorcontrib><creatorcontrib>Kasapoğulları, Deniz</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Environmental monitoring and assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mert, Berna Kırıl</au><au>Kasapoğulları, Deniz</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik</atitle><jtitle>Environmental monitoring and assessment</jtitle><stitle>Environ Monit Assess</stitle><addtitle>Environ Monit Assess</addtitle><date>2024-06-01</date><risdate>2024</risdate><volume>196</volume><issue>6</issue><spage>586</spage><epage>586</epage><pages>586-586</pages><artnum>586</artnum><issn>0167-6369</issn><issn>1573-2959</issn><eissn>1573-2959</eissn><abstract>Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentrations in Iznik Lake, which is an important water source for the surrounding communities, characterization data were taken from five different water sources flowing into the lake between 2015 and 2021. These characterization results were evaluated with IBM SPSS Statistics 23 software, with the addition of the lake water quality system. For this purpose, seven distinct physicochemical parameters were measured and monitored in Karasu, Kırandere, Olukdere and Sölöz water sources flowing into the lake, to serve as input data. Concentration levels of 15 distinct heavy metals in Karsak Stream originating from the lake were as the output. Specifically, Sn for Karasu (0.999), Sb for Kırandere (1.000), Cr for Olukdere (1.000) and Pb and Se for Sölöz (0.995) indicate parameter estimation
R
2
coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>38809274</pmid><doi>10.1007/s10661-024-12730-y</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Antimony Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution case studies Chromium computer software Earth and Environmental Science Ecology ecosystems Ecotoxicology Environment Environmental Management Environmental Monitoring Heavy metals Independent variables Lake water Lake water quality Lakes Lakes - chemistry Lead Metal concentrations Metals, Heavy - analysis Monitoring/Environmental Analysis Neural networks Neural Networks, Computer Parameter estimation Parameters Physicochemical processes Physicochemical properties Pollution prediction Selenium statistics Stream pollution streams Sustainable ecosystems temperature Tin Turkey Water budget Water Pollutants, Chemical - analysis Water Pollution, Chemical - statistics & numerical data Water Quality Water resources Water sources |
| title | A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik |
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