A non-uniform spatiotemporal kriging interpolation algorithm for landslide displacement data
The analysis of landslides using monitoring data is a commonly used method for landslide prediction and early warning. However, the loss of data due to breakdown of the monitoring equipment or interference of external factors is unavoidable in the process of monitoring landslide data. An interpolati...
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| Veröffentlicht in: | Bulletin of engineering geology and the environment 2019-09, Vol.78 (6), p.4153-4166 |
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| creator | Liu, Yong Chen, Zhe Hu, BaoDan Jin, JingKun Wu, Zhao |
| description | The analysis of landslides using monitoring data is a commonly used method for landslide prediction and early warning. However, the loss of data due to breakdown of the monitoring equipment or interference of external factors is unavoidable in the process of monitoring landslide data. An interpolation algorithm can supplement and correct the data to solve the problem of data loss. This multi-position and long-term monitoring data is non-linear, multidimensional and time-varying, which makes it difficult for the commonly used spatiotemporal kriging interpolation methods to construct an appropriate model straightaway. This paper presents a non-uniform spatiotemporal kriging interpolation method. It breaks through the restriction of Euclidean distance in the spatial dimension while breaking away from linear relationship in the temporal dimension. The spatiotemporal deformation field model is constructed using spatiotemporal optimal weights combination and subsequently optimized by particle swarm optimization algorithm. The ordinary kriging interpolation is extended to the non-uniform spatiotemporal kriging interpolation under the spatiotemporal constraints condition. This method is successfully applied to the interpolation of the monitoring data of landslide displacement. It provides better data for studies of landslide disasters and is of great practical significance for prevention and prediction of landslide disasters. |
| doi_str_mv | 10.1007/s10064-018-1388-1 |
| format | Article |
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However, the loss of data due to breakdown of the monitoring equipment or interference of external factors is unavoidable in the process of monitoring landslide data. An interpolation algorithm can supplement and correct the data to solve the problem of data loss. This multi-position and long-term monitoring data is non-linear, multidimensional and time-varying, which makes it difficult for the commonly used spatiotemporal kriging interpolation methods to construct an appropriate model straightaway. This paper presents a non-uniform spatiotemporal kriging interpolation method. It breaks through the restriction of Euclidean distance in the spatial dimension while breaking away from linear relationship in the temporal dimension. The spatiotemporal deformation field model is constructed using spatiotemporal optimal weights combination and subsequently optimized by particle swarm optimization algorithm. The ordinary kriging interpolation is extended to the non-uniform spatiotemporal kriging interpolation under the spatiotemporal constraints condition. This method is successfully applied to the interpolation of the monitoring data of landslide displacement. It provides better data for studies of landslide disasters and is of great practical significance for prevention and prediction of landslide disasters.</description><identifier>ISSN: 1435-9529</identifier><identifier>EISSN: 1435-9537</identifier><identifier>DOI: 10.1007/s10064-018-1388-1</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Data ; Data loss ; Data processing ; Deformation ; Disasters ; Displacement ; Earth and Environmental Science ; Earth Sciences ; Euclidean geometry ; Foundations ; Geoecology/Natural Processes ; Geoengineering ; Geological engineering ; Geotechnical Engineering & Applied Earth Sciences ; Hydraulics ; Interpolation ; Interpolation methods ; Kriging interpolation ; Landslides ; Landslides & mudslides ; Mathematical models ; Monitoring ; Nature Conservation ; Original Paper ; Particle swarm optimization ; Statistical methods</subject><ispartof>Bulletin of engineering geology and the environment, 2019-09, Vol.78 (6), p.4153-4166</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Bulletin of Engineering Geology and the Environment is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-8ef9317acd4b8f6f66022f96496e26f059558fe0d3462c3443a7c77bd2970f513</citedby><cites>FETCH-LOGICAL-c316t-8ef9317acd4b8f6f66022f96496e26f059558fe0d3462c3443a7c77bd2970f513</cites><orcidid>0000-0001-5326-2627</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10064-018-1388-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10064-018-1388-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51298</link.rule.ids></links><search><creatorcontrib>Liu, Yong</creatorcontrib><creatorcontrib>Chen, Zhe</creatorcontrib><creatorcontrib>Hu, BaoDan</creatorcontrib><creatorcontrib>Jin, JingKun</creatorcontrib><creatorcontrib>Wu, Zhao</creatorcontrib><title>A non-uniform spatiotemporal kriging interpolation algorithm for landslide displacement data</title><title>Bulletin of engineering geology and the environment</title><addtitle>Bull Eng Geol Environ</addtitle><description>The analysis of landslides using monitoring data is a commonly used method for landslide prediction and early warning. However, the loss of data due to breakdown of the monitoring equipment or interference of external factors is unavoidable in the process of monitoring landslide data. An interpolation algorithm can supplement and correct the data to solve the problem of data loss. This multi-position and long-term monitoring data is non-linear, multidimensional and time-varying, which makes it difficult for the commonly used spatiotemporal kriging interpolation methods to construct an appropriate model straightaway. This paper presents a non-uniform spatiotemporal kriging interpolation method. It breaks through the restriction of Euclidean distance in the spatial dimension while breaking away from linear relationship in the temporal dimension. The spatiotemporal deformation field model is constructed using spatiotemporal optimal weights combination and subsequently optimized by particle swarm optimization algorithm. The ordinary kriging interpolation is extended to the non-uniform spatiotemporal kriging interpolation under the spatiotemporal constraints condition. This method is successfully applied to the interpolation of the monitoring data of landslide displacement. It provides better data for studies of landslide disasters and is of great practical significance for prevention and prediction of landslide disasters.</description><subject>Algorithms</subject><subject>Data</subject><subject>Data loss</subject><subject>Data processing</subject><subject>Deformation</subject><subject>Disasters</subject><subject>Displacement</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Euclidean geometry</subject><subject>Foundations</subject><subject>Geoecology/Natural Processes</subject><subject>Geoengineering</subject><subject>Geological engineering</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydraulics</subject><subject>Interpolation</subject><subject>Interpolation methods</subject><subject>Kriging interpolation</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Mathematical models</subject><subject>Monitoring</subject><subject>Nature Conservation</subject><subject>Original Paper</subject><subject>Particle swarm optimization</subject><subject>Statistical methods</subject><issn>1435-9529</issn><issn>1435-9537</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kEtLxDAUhYMoOI7-AHcB19W8mjTLYfAFghvdCSHTJDVjm9Qks_Df21LRlZtzL9xzzoUPgEuMrjFC4iZPylmFcFNh2kxyBFaY0bqSNRXHvzuRp-As5z1CuG4IXoG3DQwxVIfgXUwDzKMuPhY7jDHpHn4k3_nQQR-KTWPs52OAuu9i8uV9gFMG9jqY3HtjofF57HVrBxsKNLroc3DidJ_txc9cg9e725ftQ_X0fP-43TxVLcW8VI11kmKhW8N2jeOOc0SIk5xJbgl3qJZ13TiLDGWctJQxqkUrxM4QKZCrMV2Dq6V3TPHzYHNR-3hIYXqpCMZEMtFQPrnw4mpTzDlZp8bkB52-FEZqhqgWiGqCqGaIam4mSyZP3tDZ9Nf8f-gbQsV1Vg</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Liu, Yong</creator><creator>Chen, Zhe</creator><creator>Hu, BaoDan</creator><creator>Jin, JingKun</creator><creator>Wu, Zhao</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</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>FR3</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-5326-2627</orcidid></search><sort><creationdate>20190901</creationdate><title>A non-uniform spatiotemporal kriging interpolation algorithm for landslide displacement data</title><author>Liu, Yong ; Chen, Zhe ; Hu, BaoDan ; Jin, JingKun ; Wu, Zhao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-8ef9317acd4b8f6f66022f96496e26f059558fe0d3462c3443a7c77bd2970f513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Data</topic><topic>Data loss</topic><topic>Data processing</topic><topic>Deformation</topic><topic>Disasters</topic><topic>Displacement</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Euclidean geometry</topic><topic>Foundations</topic><topic>Geoecology/Natural Processes</topic><topic>Geoengineering</topic><topic>Geological engineering</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydraulics</topic><topic>Interpolation</topic><topic>Interpolation methods</topic><topic>Kriging interpolation</topic><topic>Landslides</topic><topic>Landslides & mudslides</topic><topic>Mathematical models</topic><topic>Monitoring</topic><topic>Nature Conservation</topic><topic>Original Paper</topic><topic>Particle swarm optimization</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yong</creatorcontrib><creatorcontrib>Chen, Zhe</creatorcontrib><creatorcontrib>Hu, BaoDan</creatorcontrib><creatorcontrib>Jin, JingKun</creatorcontrib><creatorcontrib>Wu, Zhao</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest 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</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Environmental Science 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><collection>Environmental Science Collection</collection><collection>Environment Abstracts</collection><jtitle>Bulletin of engineering geology and the environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yong</au><au>Chen, Zhe</au><au>Hu, BaoDan</au><au>Jin, JingKun</au><au>Wu, Zhao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A non-uniform spatiotemporal kriging interpolation algorithm for landslide displacement data</atitle><jtitle>Bulletin of engineering geology and the environment</jtitle><stitle>Bull Eng Geol Environ</stitle><date>2019-09-01</date><risdate>2019</risdate><volume>78</volume><issue>6</issue><spage>4153</spage><epage>4166</epage><pages>4153-4166</pages><issn>1435-9529</issn><eissn>1435-9537</eissn><abstract>The analysis of landslides using monitoring data is a commonly used method for landslide prediction and early warning. However, the loss of data due to breakdown of the monitoring equipment or interference of external factors is unavoidable in the process of monitoring landslide data. An interpolation algorithm can supplement and correct the data to solve the problem of data loss. This multi-position and long-term monitoring data is non-linear, multidimensional and time-varying, which makes it difficult for the commonly used spatiotemporal kriging interpolation methods to construct an appropriate model straightaway. This paper presents a non-uniform spatiotemporal kriging interpolation method. It breaks through the restriction of Euclidean distance in the spatial dimension while breaking away from linear relationship in the temporal dimension. The spatiotemporal deformation field model is constructed using spatiotemporal optimal weights combination and subsequently optimized by particle swarm optimization algorithm. The ordinary kriging interpolation is extended to the non-uniform spatiotemporal kriging interpolation under the spatiotemporal constraints condition. This method is successfully applied to the interpolation of the monitoring data of landslide displacement. It provides better data for studies of landslide disasters and is of great practical significance for prevention and prediction of landslide disasters.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s10064-018-1388-1</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-5326-2627</orcidid></addata></record> |
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| subjects | Algorithms Data Data loss Data processing Deformation Disasters Displacement Earth and Environmental Science Earth Sciences Euclidean geometry Foundations Geoecology/Natural Processes Geoengineering Geological engineering Geotechnical Engineering & Applied Earth Sciences Hydraulics Interpolation Interpolation methods Kriging interpolation Landslides Landslides & mudslides Mathematical models Monitoring Nature Conservation Original Paper Particle swarm optimization Statistical methods |
| title | A non-uniform spatiotemporal kriging interpolation algorithm for landslide displacement data |
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