<front>
<journal-meta>
<publisher-id>RAS</publisher-id>
<title>Journal of Research in Biology</title>
<short_title>JRB</short_title>
<subject>Biology</subject>
<web_url> http://jresearchbiology.com/</web_url>
<journal_id_issn>2231–6280</journal_id_issn>
<journal_id_issn_online>2231- 6299</journal_id_issn_online>
<publisher-name>Redolence Academic Services</publisher-name>
<language>en</language>
</journal-meta>
<article-meta>
<volume>10</volume>
<number>3</number>
<publish_type>online</publish_type>
<article_type>fulltext</article_type>
<articleset>
<article>
<language>en</language>
<title> Determining the most optimal interpolation algorithm for groundwater data analysis</title>
<subject>Biology</subject>
<content_type>Short Communication</content_type>
<start_page> 2817</start_page> 
<end_page> 2825</end_page>
<web_url>http://jresearchbiology.com/documents/RA0717.pdf</web_url>
<author_list>
<first_author>
<first_name>Mohsen</first_name>
<middle_name/>
<last_name>Hedarogle</last_name>
<coreauthor>No</coreauthor>
<affiliation>Department of Environmental Sciences, Ardabil Branch, Islamic Azad University, Ardabil, Iran</affiliation>
</first_author> 
<second_author>
<first_name>Ebrahim</first_name>
<middle_name/>
<last_name>Fataei</last_name>
<coreauthor>Yes</coreauthor>
<affiliation>Department of Environmental Sciences, Ardabil Branch, Islamic Azad University, Ardabil, Iran</affiliation>
</second_author>
</author_list>
<pubdate>
<year>2020</year>
<month>04</month>
<day>18</day>
</pubdate>
<accdate>
<year>2020</year>
<month>04</month>
<day>06</day>
</accdate>
<recdate>
<year>2020</year>
<month>03</month>
<day>16</day>
</recdate>
<permissions>
<copyright-statement>Copyright 2020, Journal of Research in Biology</copyright-statement>
<copyright-year>2020</copyright-year>
<license>
<license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions>
<abstract>Contamination of heavy metals in ground water wells need to be reviewed and determination of the spatial distribution of the emissions are inevitable. For this purpose, the use of geostatistical techniques are very practical. In this study, to examine the spatial distribution of emissions of heavy metals, chromium, cadmium and lead in drinking water wells in the rural area of Meshkinshahr in high water and low water periods, different geostatistical methods were used. In this research, eight different interpolation algorithms were used in GIS and the most efficient algorithm to select the least square error function and maps related to the spatial distribution of heavy metals were drawn. The results showed that Kriging algorithm is the most optimal interpolation algorithm for groundwater data</abstract>
<keyword>Potable water, Heavy metals, Kriging algorithm, Meshkinshahr</keyword> 
</article>
</articleset>
</article-meta>
</front>