Overcoming Data Scarcity in Earth Science

• Autor principal: Etcheverry Venturini, Lorena (auth)
• Altres autors: Chreties Ceriani, Christian (auth), Castro Casales, Alberto (auth), Gorgoglione, Angela (auth)
• Format: Electrònic Capítol de llibre
• Idioma: anglès
• Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Matèries: History of engineering & technology; 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
• Accés en línia: DOAB: download the publication; DOAB: description of the publication

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...