LEADER 04026nam 2200757z- 450 001 9910404080803321 005 20231214132823.0 010 $a3-03928-211-5 035 $a(CKB)4100000011302330 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/55528 035 $a(EXLCZ)994100000011302330 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOvercoming Data Scarcity in Earth Science 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (94 p.) 311 $a3-03928-210-7 330 $aheavily 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. 610 $ageophysical monitoring 610 $adata scarcity 610 $amissing data 610 $aclimate extreme indices (CEIs) 610 $arule extraction 610 $aDataset Licensedatabase 610 $adata assimilation 610 $adata imputation 610 $asupport vector machines 610 $aenvironmental observations 610 $amulti-class classification 610 $aearth-science data 610 $aremote sensing 610 $amagnetotelluric monitoring 610 $asoil texture calculator 610 $amachine learning 610 $aClimPACT 610 $ainvasive species 610 $aspecies distribution modeling 610 $a3D-Var 610 $aensemble learning 610 $adata quality 610 $awater quality 610 $amicrohabitat 610 $ak-Nearest Neighbors 610 $aExpert Team on Climate Change Detection and Indices (ETCCDI) 610 $adecision trees 610 $aprocessing 610 $aattribute reduction 610 $aExpert Team on Sector-specific Climate Indices (ET-SCI) 610 $acore attribute 610 $arough set theory 610 $aGLDAS 610 $aarthropod vector 610 $aenvironmental modeling 610 $astatistical methods 700 $aEtcheverry Venturini$b Lorena$4auth$01325393 702 $aChreties Ceriani$b Christian$4auth 702 $aCastro Casales$b Alberto$4auth 702 $aGorgoglione$b Angela$4auth 906 $aBOOK 912 $a9910404080803321 996 $aOvercoming Data Scarcity in Earth Science$93036840 997 $aUNINA