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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Year of Publication: | 2020 |
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Physical Description: | 1 electronic resource (94 p.) |
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Etcheverry Venturini, Lorena auth Overcoming Data Scarcity in Earth Science MDPI - Multidisciplinary Digital Publishing Institute 2020 1 electronic resource (94 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. English 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 3-03928-210-7 Chreties Ceriani, Christian auth Castro Casales, Alberto auth Gorgoglione, Angela auth |
language |
English |
format |
eBook |
author |
Etcheverry Venturini, Lorena |
spellingShingle |
Etcheverry Venturini, Lorena Overcoming Data Scarcity in Earth Science |
author_facet |
Etcheverry Venturini, Lorena Chreties Ceriani, Christian Castro Casales, Alberto Gorgoglione, Angela |
author_variant |
v l e vl vle |
author2 |
Chreties Ceriani, Christian Castro Casales, Alberto Gorgoglione, Angela |
author2_variant |
c c c cc ccc c a c ca cac a g ag |
author_sort |
Etcheverry Venturini, Lorena |
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_auth |
Overcoming Data Scarcity in Earth Science |
title_new |
Overcoming Data Scarcity in Earth Science |
title_sort |
overcoming data scarcity in earth science |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
physical |
1 electronic resource (94 p.) |
isbn |
3-03928-211-5 3-03928-210-7 |
illustrated |
Not Illustrated |
work_keys_str_mv |
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is_hierarchy_title |
Overcoming Data Scarcity in Earth Science |
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