Redesigning and monitoring groundwater quality and quantity networks by using the entropy theory
This study aimed at redesigning and monitoring the groundwater network of Naqadeh plain in the southwest of Lake Urmia to examine the number and position of optimal wells for the salinity information transfer (EC) and survey of groundwater level at aquifer. In this regard, groundwater level data (35...
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description | This study aimed at redesigning and monitoring the groundwater network of Naqadeh plain in the southwest of Lake Urmia to examine the number and position of optimal wells for the salinity information transfer (EC) and survey of groundwater level at aquifer. In this regard, groundwater level data (35 wells) and electrical conductivity values (24 wells) were used during a 10-year period (2002–2012). In the first stage, simulation was conducted using the multivariate regression method and quantitative and qualitative values and the interaction of wells was observed. In the next stage, number of different classes was considered for clustering quantitative and quantitative values. The results of studying different classes of data clustering showed that the 12-class cluster had more accurate results based on the root mean square error and coefficient of determination. The root mean square error was improved by about 40, 21, and 15%, respectively, compared to the 3, 5, and 9-classe clusters. Finally, by choosing proper cluster of data, entropy indicators were investigated for quantitative and qualitative values at the aquifer level. The results of entropy indices at the aquifer showed that there was a severe shortage of information in terms of salinity in the Northwest of the aquifer, which necessitates drilling a new well in this area to accurately monitor the EC values. However, since more than 90% of the basin area is in surplus and approximately surplus conditions in terms of transferring information, the studied area has a good dispersion for qualitative monitoring. Information transfer index for the quantitative groundwater network monitoring showed that piezometers near Lake Urmia were faced with a lack of information, which according to piezometers ranking, is ranked last in terms of value of maintaining or keeping the network. Eastern areas of aquifer are also faced with shortage of piezometers accounting for about 3% of the total area. The results of survey of surplus wells in the aquifer showed that nine and six surplus wells are in the aquifer for the qualitative and quantitative network, respectively. There were also wells in which information transfer was not well done and their information could not be assured. Finally, based on the conditions, a new arrangement of wells and a new optimal network were proposed. |
doi_str_mv | 10.1007/s10661-019-7370-y |
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In this regard, groundwater level data (35 wells) and electrical conductivity values (24 wells) were used during a 10-year period (2002–2012). In the first stage, simulation was conducted using the multivariate regression method and quantitative and qualitative values and the interaction of wells was observed. In the next stage, number of different classes was considered for clustering quantitative and quantitative values. The results of studying different classes of data clustering showed that the 12-class cluster had more accurate results based on the root mean square error and coefficient of determination. The root mean square error was improved by about 40, 21, and 15%, respectively, compared to the 3, 5, and 9-classe clusters. Finally, by choosing proper cluster of data, entropy indicators were investigated for quantitative and qualitative values at the aquifer level. The results of entropy indices at the aquifer showed that there was a severe shortage of information in terms of salinity in the Northwest of the aquifer, which necessitates drilling a new well in this area to accurately monitor the EC values. However, since more than 90% of the basin area is in surplus and approximately surplus conditions in terms of transferring information, the studied area has a good dispersion for qualitative monitoring. Information transfer index for the quantitative groundwater network monitoring showed that piezometers near Lake Urmia were faced with a lack of information, which according to piezometers ranking, is ranked last in terms of value of maintaining or keeping the network. Eastern areas of aquifer are also faced with shortage of piezometers accounting for about 3% of the total area. The results of survey of surplus wells in the aquifer showed that nine and six surplus wells are in the aquifer for the qualitative and quantitative network, respectively. There were also wells in which information transfer was not well done and their information could not be assured. Finally, based on the conditions, a new arrangement of wells and a new optimal network were proposed.</description><identifier>ISSN: 0167-6369</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-019-7370-y</identifier><identifier>PMID: 30919110</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Aquifers ; Area ; Atmospheric Protection/Air Quality Control/Air Pollution ; Clustering ; Data ; Drilling ; Earth and Environmental Science ; Ecology ; Ecotoxicology ; Electrical conductivity ; Electrical resistivity ; Entropy ; Environment ; Environmental Management ; Environmental Monitoring ; Environmental science ; Groundwater ; Groundwater - analysis ; Groundwater data ; Groundwater levels ; Groundwater quality ; Information transfer ; Iran ; Lakes ; Mean square errors ; Monitoring ; Monitoring/Environmental Analysis ; Piezometers ; Qualitative analysis ; Root-mean-square errors ; Salinity ; Salinity effects ; Surveying ; Water monitoring ; Water quality ; Water Wells</subject><ispartof>Environmental monitoring and assessment, 2019-04, Vol.191 (4), p.250-17, Article 250</ispartof><rights>Springer Nature Switzerland AG 2019</rights><rights>Environmental Monitoring and Assessment is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-be958aa5e32d739516faee2ad7276a374e7834ff973700a30768f975cafbbcd63</citedby><cites>FETCH-LOGICAL-c372t-be958aa5e32d739516faee2ad7276a374e7834ff973700a30768f975cafbbcd63</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/s10661-019-7370-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-019-7370-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30919110$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nazeri Tahroudi, Mohammad</creatorcontrib><creatorcontrib>Khashei Siuki, Abbas</creatorcontrib><creatorcontrib>Ramezani, Yousef</creatorcontrib><title>Redesigning and monitoring groundwater quality and quantity networks by using the entropy theory</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><addtitle>Environ Monit Assess</addtitle><description>This study aimed at redesigning and monitoring the groundwater network of Naqadeh plain in the southwest of Lake Urmia to examine the number and position of optimal wells for the salinity information transfer (EC) and survey of groundwater level at aquifer. In this regard, groundwater level data (35 wells) and electrical conductivity values (24 wells) were used during a 10-year period (2002–2012). In the first stage, simulation was conducted using the multivariate regression method and quantitative and qualitative values and the interaction of wells was observed. In the next stage, number of different classes was considered for clustering quantitative and quantitative values. The results of studying different classes of data clustering showed that the 12-class cluster had more accurate results based on the root mean square error and coefficient of determination. The root mean square error was improved by about 40, 21, and 15%, respectively, compared to the 3, 5, and 9-classe clusters. Finally, by choosing proper cluster of data, entropy indicators were investigated for quantitative and qualitative values at the aquifer level. The results of entropy indices at the aquifer showed that there was a severe shortage of information in terms of salinity in the Northwest of the aquifer, which necessitates drilling a new well in this area to accurately monitor the EC values. However, since more than 90% of the basin area is in surplus and approximately surplus conditions in terms of transferring information, the studied area has a good dispersion for qualitative monitoring. Information transfer index for the quantitative groundwater network monitoring showed that piezometers near Lake Urmia were faced with a lack of information, which according to piezometers ranking, is ranked last in terms of value of maintaining or keeping the network. Eastern areas of aquifer are also faced with shortage of piezometers accounting for about 3% of the total area. The results of survey of surplus wells in the aquifer showed that nine and six surplus wells are in the aquifer for the qualitative and quantitative network, respectively. There were also wells in which information transfer was not well done and their information could not be assured. Finally, based on the conditions, a new arrangement of wells and a new optimal network were proposed.</description><subject>Aquifers</subject><subject>Area</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Clustering</subject><subject>Data</subject><subject>Drilling</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Ecotoxicology</subject><subject>Electrical conductivity</subject><subject>Electrical resistivity</subject><subject>Entropy</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Environmental Monitoring</subject><subject>Environmental science</subject><subject>Groundwater</subject><subject>Groundwater - analysis</subject><subject>Groundwater data</subject><subject>Groundwater levels</subject><subject>Groundwater quality</subject><subject>Information transfer</subject><subject>Iran</subject><subject>Lakes</subject><subject>Mean square errors</subject><subject>Monitoring</subject><subject>Monitoring/Environmental Analysis</subject><subject>Piezometers</subject><subject>Qualitative analysis</subject><subject>Root-mean-square errors</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Surveying</subject><subject>Water monitoring</subject><subject>Water quality</subject><subject>Water Wells</subject><issn>0167-6369</issn><issn>1573-2959</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp1kN1LwzAUxYMobk7_AF-k4Isv1aRZc5tHEb9gIIg-x7S9nZ1bsiUp0v_exvkBgk-5B37n5N5DyDGj54xSuPCMCsFSymQKHGja75Axy4GnmczlLhlTJiAVXMgROfB-QSmVMJX7ZMSpZJIxOiYvj1ijb-emNfNEmzpZWdMG66KcO9uZ-l0HdMmm08s29J_IMJsQhcHwbt2bT8o-6Xy0hFdM0ARn132cresPyV6jlx6Pvt4Jeb65frq6S2cPt_dXl7O04pCFtESZF1rnyLMauMyZaDRipmvIQGgOU4SCT5tGxkOp5hREMYi80k1ZVrXgE3K2zV07u-nQB7VqfYXLpTZoO68yJiUrIBvqmZDTP-jCds4M20UKJICUxUCxLVU5673DRq1du9KuV4yqWL_a1q-G-lXcSvWD5-QruStXWP84vvsegGwL-HWsGN3v1_-nfgAW9ZIt</recordid><startdate>20190401</startdate><enddate>20190401</enddate><creator>Nazeri Tahroudi, Mohammad</creator><creator>Khashei Siuki, Abbas</creator><creator>Ramezani, Yousef</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>3V.</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>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>KL.</scope><scope>L.-</scope><scope>L.G</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>P64</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><scope>7X8</scope></search><sort><creationdate>20190401</creationdate><title>Redesigning and monitoring groundwater quality and quantity networks by using the entropy theory</title><author>Nazeri Tahroudi, Mohammad ; Khashei Siuki, Abbas ; Ramezani, Yousef</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-be958aa5e32d739516faee2ad7276a374e7834ff973700a30768f975cafbbcd63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Aquifers</topic><topic>Area</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Clustering</topic><topic>Data</topic><topic>Drilling</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>Ecotoxicology</topic><topic>Electrical conductivity</topic><topic>Electrical resistivity</topic><topic>Entropy</topic><topic>Environment</topic><topic>Environmental Management</topic><topic>Environmental Monitoring</topic><topic>Environmental science</topic><topic>Groundwater</topic><topic>Groundwater - analysis</topic><topic>Groundwater data</topic><topic>Groundwater levels</topic><topic>Groundwater quality</topic><topic>Information transfer</topic><topic>Iran</topic><topic>Lakes</topic><topic>Mean square errors</topic><topic>Monitoring</topic><topic>Monitoring/Environmental Analysis</topic><topic>Piezometers</topic><topic>Qualitative analysis</topic><topic>Root-mean-square errors</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Surveying</topic><topic>Water monitoring</topic><topic>Water quality</topic><topic>Water Wells</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nazeri Tahroudi, Mohammad</creatorcontrib><creatorcontrib>Khashei Siuki, Abbas</creatorcontrib><creatorcontrib>Ramezani, Yousef</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</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>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Natural 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>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><jtitle>Environmental monitoring and assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nazeri Tahroudi, Mohammad</au><au>Khashei Siuki, Abbas</au><au>Ramezani, Yousef</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Redesigning and monitoring groundwater quality and quantity networks by using the entropy theory</atitle><jtitle>Environmental monitoring and assessment</jtitle><stitle>Environ Monit Assess</stitle><addtitle>Environ Monit Assess</addtitle><date>2019-04-01</date><risdate>2019</risdate><volume>191</volume><issue>4</issue><spage>250</spage><epage>17</epage><pages>250-17</pages><artnum>250</artnum><issn>0167-6369</issn><eissn>1573-2959</eissn><abstract>This study aimed at redesigning and monitoring the groundwater network of Naqadeh plain in the southwest of Lake Urmia to examine the number and position of optimal wells for the salinity information transfer (EC) and survey of groundwater level at aquifer. In this regard, groundwater level data (35 wells) and electrical conductivity values (24 wells) were used during a 10-year period (2002–2012). In the first stage, simulation was conducted using the multivariate regression method and quantitative and qualitative values and the interaction of wells was observed. In the next stage, number of different classes was considered for clustering quantitative and quantitative values. The results of studying different classes of data clustering showed that the 12-class cluster had more accurate results based on the root mean square error and coefficient of determination. The root mean square error was improved by about 40, 21, and 15%, respectively, compared to the 3, 5, and 9-classe clusters. Finally, by choosing proper cluster of data, entropy indicators were investigated for quantitative and qualitative values at the aquifer level. The results of entropy indices at the aquifer showed that there was a severe shortage of information in terms of salinity in the Northwest of the aquifer, which necessitates drilling a new well in this area to accurately monitor the EC values. However, since more than 90% of the basin area is in surplus and approximately surplus conditions in terms of transferring information, the studied area has a good dispersion for qualitative monitoring. Information transfer index for the quantitative groundwater network monitoring showed that piezometers near Lake Urmia were faced with a lack of information, which according to piezometers ranking, is ranked last in terms of value of maintaining or keeping the network. Eastern areas of aquifer are also faced with shortage of piezometers accounting for about 3% of the total area. The results of survey of surplus wells in the aquifer showed that nine and six surplus wells are in the aquifer for the qualitative and quantitative network, respectively. There were also wells in which information transfer was not well done and their information could not be assured. Finally, based on the conditions, a new arrangement of wells and a new optimal network were proposed.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>30919110</pmid><doi>10.1007/s10661-019-7370-y</doi><tpages>17</tpages></addata></record> |
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subjects | Aquifers Area Atmospheric Protection/Air Quality Control/Air Pollution Clustering Data Drilling Earth and Environmental Science Ecology Ecotoxicology Electrical conductivity Electrical resistivity Entropy Environment Environmental Management Environmental Monitoring Environmental science Groundwater Groundwater - analysis Groundwater data Groundwater levels Groundwater quality Information transfer Iran Lakes Mean square errors Monitoring Monitoring/Environmental Analysis Piezometers Qualitative analysis Root-mean-square errors Salinity Salinity effects Surveying Water monitoring Water quality Water Wells |
title | Redesigning and monitoring groundwater quality and quantity networks by using the entropy theory |
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