Application of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data
The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial inte...
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| Veröffentlicht in: | PloS one 2022-04, Vol.17 (4), p.e0266942-e0266942 |
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| creator | Li, Yang Baorong, Zhong Xiaohong, Xu Zijun, Liang |
| description | The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial interpolation. A deep neural network is a machine learning algorithm that can, in principle, be applied to any function, including a semivariogram. Accordingly, a novel spatial interpolation method based on a deep neural network and Ordinary Kriging was proposed in this research, and elevation data were used as a case study. Compared with the semivariogram fitted by the traditional exponential model, spherical model, and Gaussian model, the kriging variance in the proposed method is smaller, which means that the interpolation results are closer to the theoretical results of Ordinary Kriging interpolation. At the same time, this research can simplify processes for a variety of semivariogram analyses. |
| doi_str_mv | 10.1371/journal.pone.0266942 |
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Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial interpolation. A deep neural network is a machine learning algorithm that can, in principle, be applied to any function, including a semivariogram. Accordingly, a novel spatial interpolation method based on a deep neural network and Ordinary Kriging was proposed in this research, and elevation data were used as a case study. Compared with the semivariogram fitted by the traditional exponential model, spherical model, and Gaussian model, the kriging variance in the proposed method is smaller, which means that the interpolation results are closer to the theoretical results of Ordinary Kriging interpolation. At the same time, this research can simplify processes for a variety of semivariogram analyses.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0266942</identifier><identifier>PMID: 35452466</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Analysis ; Artificial neural networks ; Biology and Life Sciences ; Computer and Information Sciences ; Data mining ; Datasets ; Earth Sciences ; Geostatistics ; Interpolation ; Kriging interpolation ; Li, Yang ; Machine learning ; Maximum likelihood method ; Neural networks ; Normal distribution ; Physical Sciences ; Research and Analysis Methods</subject><ispartof>PloS one, 2022-04, Vol.17 (4), p.e0266942-e0266942</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Li et al 2022 Li et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c622t-5cb7c22ed4fb0edfee41a1127f334ae92d9ac63faed9081bc02580de15f6e2183</citedby><cites>FETCH-LOGICAL-c622t-5cb7c22ed4fb0edfee41a1127f334ae92d9ac63faed9081bc02580de15f6e2183</cites><orcidid>0000-0002-9442-7704</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9032399/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9032399/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35452466$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Nyeem, Hussain Md Abu</contributor><creatorcontrib>Li, Yang</creatorcontrib><creatorcontrib>Baorong, Zhong</creatorcontrib><creatorcontrib>Xiaohong, Xu</creatorcontrib><creatorcontrib>Zijun, Liang</creatorcontrib><title>Application of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial interpolation. A deep neural network is a machine learning algorithm that can, in principle, be applied to any function, including a semivariogram. Accordingly, a novel spatial interpolation method based on a deep neural network and Ordinary Kriging was proposed in this research, and elevation data were used as a case study. Compared with the semivariogram fitted by the traditional exponential model, spherical model, and Gaussian model, the kriging variance in the proposed method is smaller, which means that the interpolation results are closer to the theoretical results of Ordinary Kriging interpolation. At the same time, this research can simplify processes for a variety of semivariogram analyses.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Data mining</subject><subject>Datasets</subject><subject>Earth Sciences</subject><subject>Geostatistics</subject><subject>Interpolation</subject><subject>Kriging interpolation</subject><subject>Li, Yang</subject><subject>Machine learning</subject><subject>Maximum likelihood method</subject><subject>Neural networks</subject><subject>Normal distribution</subject><subject>Physical Sciences</subject><subject>Research and Analysis 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Abu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-04-22</date><risdate>2022</risdate><volume>17</volume><issue>4</issue><spage>e0266942</spage><epage>e0266942</epage><pages>e0266942-e0266942</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial interpolation. A deep neural network is a machine learning algorithm that can, in principle, be applied to any function, including a semivariogram. Accordingly, a novel spatial interpolation method based on a deep neural network and Ordinary Kriging was proposed in this research, and elevation data were used as a case study. Compared with the semivariogram fitted by the traditional exponential model, spherical model, and Gaussian model, the kriging variance in the proposed method is smaller, which means that the interpolation results are closer to the theoretical results of Ordinary Kriging interpolation. At the same time, this research can simplify processes for a variety of semivariogram analyses.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35452466</pmid><doi>10.1371/journal.pone.0266942</doi><tpages>e0266942</tpages><orcidid>https://orcid.org/0000-0002-9442-7704</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Analysis Artificial neural networks Biology and Life Sciences Computer and Information Sciences Data mining Datasets Earth Sciences Geostatistics Interpolation Kriging interpolation Li, Yang Machine learning Maximum likelihood method Neural networks Normal distribution Physical Sciences Research and Analysis Methods |
| title | Application of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data |
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