Overcoming Data Scarcity in Earth Science
heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable...
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| Auteurs principaux: | , , , |
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| Format: | Online |
| Langue: | anglais |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Accès en ligne: | 46098 |
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| _version_ | 1869523371994120192 |
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| author | Etcheverry Venturini, Lorena Chreties Ceriani, Christian Castro Casales, Alberto Gorgoglione, Angela |
| author_browse | Castro Casales, Alberto Chreties Ceriani, Christian Etcheverry Venturini, Lorena Gorgoglione, Angela |
| author_facet | Etcheverry Venturini, Lorena Chreties Ceriani, Christian Castro Casales, Alberto Gorgoglione, Angela |
| author_sort | Etcheverry Venturini, Lorena |
| collection | Directory of Open Access Books |
| description | heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales. |
| format | Online |
| id | doab-20.500.12854ir-55528 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-555282024-04-11T15:10:23Z Overcoming Data Scarcity in Earth Science Etcheverry Venturini, Lorena Chreties Ceriani, Christian Castro Casales, Alberto Gorgoglione, Angela TA1-2040 T1-995 TA170-171 geophysical monitoring data scarcity missing data climate extreme indices (CEIs) rule extraction Dataset Licensedatabase data assimilation data imputation support vector machines environmental observations multi-class classification earth-science data remote sensing magnetotelluric monitoring soil texture calculator machine learning ClimPACT invasive species species distribution modeling 3D-Var ensemble learning data quality water quality microhabitat k-Nearest Neighbors Expert Team on Climate Change Detection and Indices (ETCCDI) decision trees processing attribute reduction Expert Team on Sector-specific Climate Indices (ET-SCI) core attribute rough set theory GLDAS arthropod vector environmental modeling statistical methods thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales. 2021-02-11T22:01:02Z 2021-02-11T22:01:02Z 2020-06-09 16:38:57 2020 book 46098 9783039282111 9783039282104 https://directory.doabooks.org/handle/20.500.12854/55528 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/2292 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03928-211-1 10.3390/books978-3-03928-211-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039282111 9783039282104 94 open access |
| spellingShingle | TA1-2040 T1-995 TA170-171 geophysical monitoring data scarcity missing data climate extreme indices (CEIs) rule extraction Dataset Licensedatabase data assimilation data imputation support vector machines environmental observations multi-class classification earth-science data remote sensing magnetotelluric monitoring soil texture calculator machine learning ClimPACT invasive species species distribution modeling 3D-Var ensemble learning data quality water quality microhabitat k-Nearest Neighbors Expert Team on Climate Change Detection and Indices (ETCCDI) decision trees processing attribute reduction Expert Team on Sector-specific Climate Indices (ET-SCI) core attribute rough set theory GLDAS arthropod vector environmental modeling statistical methods thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Etcheverry Venturini, Lorena Chreties Ceriani, Christian Castro Casales, Alberto Gorgoglione, Angela Overcoming Data Scarcity in Earth Science |
| title | Overcoming Data Scarcity in Earth Science |
| title_full | Overcoming Data Scarcity in Earth Science |
| title_fullStr | Overcoming Data Scarcity in Earth Science |
| title_full_unstemmed | Overcoming Data Scarcity in Earth Science |
| title_short | Overcoming Data Scarcity in Earth Science |
| title_sort | overcoming data scarcity in earth science |
| topic | TA1-2040 T1-995 TA170-171 geophysical monitoring data scarcity missing data climate extreme indices (CEIs) rule extraction Dataset Licensedatabase data assimilation data imputation support vector machines environmental observations multi-class classification earth-science data remote sensing magnetotelluric monitoring soil texture calculator machine learning ClimPACT invasive species species distribution modeling 3D-Var ensemble learning data quality water quality microhabitat k-Nearest Neighbors Expert Team on Climate Change Detection and Indices (ETCCDI) decision trees processing attribute reduction Expert Team on Sector-specific Climate Indices (ET-SCI) core attribute rough set theory GLDAS arthropod vector environmental modeling statistical methods thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | TA1-2040 T1-995 TA170-171 geophysical monitoring data scarcity missing data climate extreme indices (CEIs) rule extraction Dataset Licensedatabase data assimilation data imputation support vector machines environmental observations multi-class classification earth-science data remote sensing magnetotelluric monitoring soil texture calculator machine learning ClimPACT invasive species species distribution modeling 3D-Var ensemble learning data quality water quality microhabitat k-Nearest Neighbors Expert Team on Climate Change Detection and Indices (ETCCDI) decision trees processing attribute reduction Expert Team on Sector-specific Climate Indices (ET-SCI) core attribute rough set theory GLDAS arthropod vector environmental modeling statistical methods thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | 46098 |
| work_keys_str_mv | AT etcheverryventurinilorena overcomingdatascarcityinearthscience AT chretiescerianichristian overcomingdatascarcityinearthscience AT castrocasalesalberto overcomingdatascarcityinearthscience AT gorgoglioneangela overcomingdatascarcityinearthscience |