Editorial topic collection: “Geosphere-anthroposphere interlinked dynamics: geocomputing and new technologies”
Understanding the interactions between the anthroposphere and the geosphere, such as natural hazards, land degradation, quantitative and qualitative impacts on ground and surface waters, is a challenging task. The monitoring and modelling of these interactions can be characterized by high uncertaint...
Gespeichert in:
Veröffentlicht in: | Environmental earth sciences 2023-11, Vol.82 (21), p.507, Article 507 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 21 |
container_start_page | 507 |
container_title | Environmental earth sciences |
container_volume | 82 |
creator | Trevisani, S. Cavalli, M. Tosti, F. |
description | Understanding the interactions between the anthroposphere and the geosphere, such as natural hazards, land degradation, quantitative and qualitative impacts on ground and surface waters, is a challenging task. The monitoring and modelling of these interactions can be characterized by high uncertainties in data and models, especially when considering urban areas or locations near engineering infrastructures. Technological and scientific advancements, including remote sensing, geophysical prospecting, drilling equipment, and information technology, have contributed to enhancing our current understanding of these interconnected dynamics. The availability of increasingly large datasets provides better insights into the mechanisms that govern these interactions, but it also adds complexity to monitoring, modeling, and forecasting procedures. From this viewpoint, the utilization of advanced geocomputational methodologies, such as machine learning, geostatistics, pattern recognition, geomorphometry, and other computational-based approaches, plays a pivotal role. |
doi_str_mv | 10.1007/s12665-023-11172-y |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2872526652</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2872526652</sourcerecordid><originalsourceid>FETCH-LOGICAL-a293t-873695067b40bb5339a995cb908b5679c008676111346ac01e33fd21bc9cf3503</originalsourceid><addsrcrecordid>eNp9kE1OwzAQhS0EElXpBVhZYh3wT-PE3aGqFKRKbGBtOY6TuqR2sF2h7HoQuFxPQkoq2DGbmZHee6P5ALjG6BYjlN0FTBhLE0RogjHOSNKdgRHOGUsY4fz8d87RJZiEsEF9UUw5YiPgF6WJzhvZwOhao6ByTaNVNM7O4GH_udQutGvtdSJtXHvXnlZobNS-MfZNl7DsrNwaFWaw1k65bbuLxtZQ2hJa_QGjVmvrGlcbHQ77rytwUckm6Mmpj8Hrw-Jl_pisnpdP8_tVIgmnMckzyniKWFZMUVGklHLJeaoKjvIiZRlXCOUsY_3HdMqkQlhTWpUEF4qriqaIjsHNkNt6977TIYqN23nbnxQkz0h6hEZ6FRlUyrsQvK5E681W-k5gJI54xYBX9HjFD17R9SY6mEIvtrX2f9H_uL4BN1yA7w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2872526652</pqid></control><display><type>article</type><title>Editorial topic collection: “Geosphere-anthroposphere interlinked dynamics: geocomputing and new technologies”</title><source>SpringerLink Journals</source><creator>Trevisani, S. ; Cavalli, M. ; Tosti, F.</creator><creatorcontrib>Trevisani, S. ; Cavalli, M. ; Tosti, F.</creatorcontrib><description>Understanding the interactions between the anthroposphere and the geosphere, such as natural hazards, land degradation, quantitative and qualitative impacts on ground and surface waters, is a challenging task. The monitoring and modelling of these interactions can be characterized by high uncertainties in data and models, especially when considering urban areas or locations near engineering infrastructures. Technological and scientific advancements, including remote sensing, geophysical prospecting, drilling equipment, and information technology, have contributed to enhancing our current understanding of these interconnected dynamics. The availability of increasingly large datasets provides better insights into the mechanisms that govern these interactions, but it also adds complexity to monitoring, modeling, and forecasting procedures. From this viewpoint, the utilization of advanced geocomputational methodologies, such as machine learning, geostatistics, pattern recognition, geomorphometry, and other computational-based approaches, plays a pivotal role.</description><identifier>ISSN: 1866-6280</identifier><identifier>EISSN: 1866-6299</identifier><identifier>DOI: 10.1007/s12665-023-11172-y</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Biogeosciences ; Boolean ; Civil engineering ; Drilling ; Drilling equipment ; Drilling machines (tools) ; Earth and Environmental Science ; Earth science ; Earth Sciences ; Environmental Science and Engineering ; Geochemistry ; Geology ; Geomorphology ; Geosphere ; Geostatistics ; Groundwater ; Hydrology/Water Resources ; Information technology ; Land degradation ; Machine learning ; Mars ; Monitoring ; New technology ; Original Article ; Pattern recognition ; Remote sensing ; Research methodology ; Sediments ; Surface water ; Terrestrial Pollution ; Time series ; Urban areas</subject><ispartof>Environmental earth sciences, 2023-11, Vol.82 (21), p.507, Article 507</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-a293t-873695067b40bb5339a995cb908b5679c008676111346ac01e33fd21bc9cf3503</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/s12665-023-11172-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12665-023-11172-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Trevisani, S.</creatorcontrib><creatorcontrib>Cavalli, M.</creatorcontrib><creatorcontrib>Tosti, F.</creatorcontrib><title>Editorial topic collection: “Geosphere-anthroposphere interlinked dynamics: geocomputing and new technologies”</title><title>Environmental earth sciences</title><addtitle>Environ Earth Sci</addtitle><description>Understanding the interactions between the anthroposphere and the geosphere, such as natural hazards, land degradation, quantitative and qualitative impacts on ground and surface waters, is a challenging task. The monitoring and modelling of these interactions can be characterized by high uncertainties in data and models, especially when considering urban areas or locations near engineering infrastructures. Technological and scientific advancements, including remote sensing, geophysical prospecting, drilling equipment, and information technology, have contributed to enhancing our current understanding of these interconnected dynamics. The availability of increasingly large datasets provides better insights into the mechanisms that govern these interactions, but it also adds complexity to monitoring, modeling, and forecasting procedures. From this viewpoint, the utilization of advanced geocomputational methodologies, such as machine learning, geostatistics, pattern recognition, geomorphometry, and other computational-based approaches, plays a pivotal role.</description><subject>Biogeosciences</subject><subject>Boolean</subject><subject>Civil engineering</subject><subject>Drilling</subject><subject>Drilling equipment</subject><subject>Drilling machines (tools)</subject><subject>Earth and Environmental Science</subject><subject>Earth science</subject><subject>Earth Sciences</subject><subject>Environmental Science and Engineering</subject><subject>Geochemistry</subject><subject>Geology</subject><subject>Geomorphology</subject><subject>Geosphere</subject><subject>Geostatistics</subject><subject>Groundwater</subject><subject>Hydrology/Water Resources</subject><subject>Information technology</subject><subject>Land degradation</subject><subject>Machine learning</subject><subject>Mars</subject><subject>Monitoring</subject><subject>New technology</subject><subject>Original Article</subject><subject>Pattern recognition</subject><subject>Remote sensing</subject><subject>Research methodology</subject><subject>Sediments</subject><subject>Surface water</subject><subject>Terrestrial Pollution</subject><subject>Time series</subject><subject>Urban areas</subject><issn>1866-6280</issn><issn>1866-6299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE1OwzAQhS0EElXpBVhZYh3wT-PE3aGqFKRKbGBtOY6TuqR2sF2h7HoQuFxPQkoq2DGbmZHee6P5ALjG6BYjlN0FTBhLE0RogjHOSNKdgRHOGUsY4fz8d87RJZiEsEF9UUw5YiPgF6WJzhvZwOhao6ByTaNVNM7O4GH_udQutGvtdSJtXHvXnlZobNS-MfZNl7DsrNwaFWaw1k65bbuLxtZQ2hJa_QGjVmvrGlcbHQ77rytwUckm6Mmpj8Hrw-Jl_pisnpdP8_tVIgmnMckzyniKWFZMUVGklHLJeaoKjvIiZRlXCOUsY_3HdMqkQlhTWpUEF4qriqaIjsHNkNt6977TIYqN23nbnxQkz0h6hEZ6FRlUyrsQvK5E681W-k5gJI54xYBX9HjFD17R9SY6mEIvtrX2f9H_uL4BN1yA7w</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Trevisani, S.</creator><creator>Cavalli, M.</creator><creator>Tosti, F.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>20231101</creationdate><title>Editorial topic collection: “Geosphere-anthroposphere interlinked dynamics: geocomputing and new technologies”</title><author>Trevisani, S. ; Cavalli, M. ; Tosti, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a293t-873695067b40bb5339a995cb908b5679c008676111346ac01e33fd21bc9cf3503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biogeosciences</topic><topic>Boolean</topic><topic>Civil engineering</topic><topic>Drilling</topic><topic>Drilling equipment</topic><topic>Drilling machines (tools)</topic><topic>Earth and Environmental Science</topic><topic>Earth science</topic><topic>Earth Sciences</topic><topic>Environmental Science and Engineering</topic><topic>Geochemistry</topic><topic>Geology</topic><topic>Geomorphology</topic><topic>Geosphere</topic><topic>Geostatistics</topic><topic>Groundwater</topic><topic>Hydrology/Water Resources</topic><topic>Information technology</topic><topic>Land degradation</topic><topic>Machine learning</topic><topic>Mars</topic><topic>Monitoring</topic><topic>New technology</topic><topic>Original Article</topic><topic>Pattern recognition</topic><topic>Remote sensing</topic><topic>Research methodology</topic><topic>Sediments</topic><topic>Surface water</topic><topic>Terrestrial Pollution</topic><topic>Time series</topic><topic>Urban areas</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Trevisani, S.</creatorcontrib><creatorcontrib>Cavalli, M.</creatorcontrib><creatorcontrib>Tosti, F.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</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>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 Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</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>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Science 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>ProQuest Central China</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Environmental earth sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Trevisani, S.</au><au>Cavalli, M.</au><au>Tosti, F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Editorial topic collection: “Geosphere-anthroposphere interlinked dynamics: geocomputing and new technologies”</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2023-11-01</date><risdate>2023</risdate><volume>82</volume><issue>21</issue><spage>507</spage><pages>507-</pages><artnum>507</artnum><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>Understanding the interactions between the anthroposphere and the geosphere, such as natural hazards, land degradation, quantitative and qualitative impacts on ground and surface waters, is a challenging task. The monitoring and modelling of these interactions can be characterized by high uncertainties in data and models, especially when considering urban areas or locations near engineering infrastructures. Technological and scientific advancements, including remote sensing, geophysical prospecting, drilling equipment, and information technology, have contributed to enhancing our current understanding of these interconnected dynamics. The availability of increasingly large datasets provides better insights into the mechanisms that govern these interactions, but it also adds complexity to monitoring, modeling, and forecasting procedures. From this viewpoint, the utilization of advanced geocomputational methodologies, such as machine learning, geostatistics, pattern recognition, geomorphometry, and other computational-based approaches, plays a pivotal role.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12665-023-11172-y</doi></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1866-6280 |
ispartof | Environmental earth sciences, 2023-11, Vol.82 (21), p.507, Article 507 |
issn | 1866-6280 1866-6299 |
language | eng |
recordid | cdi_proquest_journals_2872526652 |
source | SpringerLink Journals |
subjects | Biogeosciences Boolean Civil engineering Drilling Drilling equipment Drilling machines (tools) Earth and Environmental Science Earth science Earth Sciences Environmental Science and Engineering Geochemistry Geology Geomorphology Geosphere Geostatistics Groundwater Hydrology/Water Resources Information technology Land degradation Machine learning Mars Monitoring New technology Original Article Pattern recognition Remote sensing Research methodology Sediments Surface water Terrestrial Pollution Time series Urban areas |
title | Editorial topic collection: “Geosphere-anthroposphere interlinked dynamics: geocomputing and new technologies” |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T06%3A20%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Editorial%20topic%20collection:%20%E2%80%9CGeosphere-anthroposphere%20interlinked%20dynamics:%20geocomputing%20and%20new%20technologies%E2%80%9D&rft.jtitle=Environmental%20earth%20sciences&rft.au=Trevisani,%20S.&rft.date=2023-11-01&rft.volume=82&rft.issue=21&rft.spage=507&rft.pages=507-&rft.artnum=507&rft.issn=1866-6280&rft.eissn=1866-6299&rft_id=info:doi/10.1007/s12665-023-11172-y&rft_dat=%3Cproquest_cross%3E2872526652%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2872526652&rft_id=info:pmid/&rfr_iscdi=true |