Description: Das Projekt "GeoKernels: Kernel-Based Methods for Geo- and Environmental Sciences - Phase2" wird vom Umweltbundesamt gefördert und von Universite de Lausanne, Institut de geomatique et d'analyse du risque durchgeführt. This proposal is a continuation of the project 'GeoKernels': Kernel-Based Methods for Geo- and Environmental Sciences (200021-113944/1). The projects deal with the fundamental developments in the field of intelligent geospatial data analysis and modelling using Machine Learning algorithms. The first phase of the GeoKernels project provided a general methodology for using the state-of-the-art models in machine learning (where kernel methods establish one of the main successful areas) for spatio-temporal data analysis and modelling. Real life data lie on some lower-dimensional manifolds in the original high-dimensional geo-feature space. For environmental data these natural low-dimensional geo-manifolds are induced by rivers, relief features, urban structures, hydro-geological formations, etc. During PhaseI in the GeoKernels methodology, semi-supervised learning was applied to the stated problems in an efficient and elegant manner. The continuation of the project (Phase2) is aimed at advancing the data-driven GeoKernels modelling methodology, bringing it closer to the need of real-life operational use, from one side, and developing new methods concerned with geomanifold modelling by feature extraction and interpretable predictions with multiple kernel learning. The new developments will provide more transparency to the data-driven methods and will bring more flexibility for modelling complex environmental processes. The methods are particularly targeted at applications in natural hazards assessments and forecasting, topo-climatic modelling and renewable resources evaluation. Due to the established collaboration, the results of this multidisciplinary project will improve spatial data collection and management process in different scientific fields, will develop new procedures of environmental pattern recognition and modelling approaches using recent achievements in machine learning. The main results will be presented at the international conferences and workshops and published in scientific journals and books. The results, including the software modules (Machine Learning Office) and online interactive case studies will be available at the website of the project for the research and educational purposes (www.geokernels.org).
Types:
SupportProgram
Origin: /Bund/UBA/UFORDAT
Tags: Radon ? Fluss ? Lausanne ? Buchhaltung ? Pflanzensamen ? Skigebiet ? Main ? Umweltdaten ? Bodenverunreinigung ? Hydrogeologie ? Kartographie ? Regenwasser ? Software ? Stadtstruktur ? Temperatur ? Übriger Bergbau ? Prognose ? Luftverschmutzung ? Karte ? Umweltmonitoring ? Städtische Verschmutzung ? Geodaten ? Fallstudie ? Bildung ? Ackerland ? Berechnungsverfahren ? Bewässerung ? Bewertungsverfahren ? Daten ? Erneuerbare Ressource ? Geographie ? Handelsgewerbe ? Monitoring ? Rechtsschutz ? Stand der Technik ? Statistische Analyse ? Technik ? Umweltinformation ? Modellierung ? Datenmodell ? Umweltwissenschaft ? Messung ? Naturgefahr ? Geländerelief ? Künstliche Intelligenz ? Workshop ? Datenbank ? Datenerhebung ? Forschung ? Klassifikation ? Umwelt ? Maschine ? Gebirge ? Verunreinigung ? Maßnahme ? geosimulations ? EIN ? VORSCHLAG ? ANGEWANDT ? ZEITRAEUME ? ENTWICKLUNG ? FOERDERN ? data mining ? GEBRAUCHT ? GEMISCHT ? GRUNDLAGE ? LAERMINTENSIV ? geo- and environmental sciences ? Unterricht ? geostatistics ? METHODE ? MOEGLICHKEIT ? WICHTIG ? kernel-based methods ? NACHFRAGE ? BETRAG ? NEU ? PROBE ? Buchgrundstück ? SONSTIG ? Spatio-temporal data analysis ? manifold learning ? spatio-temporal data ? WISSENSCHAFT ? Werkzeug ? support vector machines ? Statistical Learning Theory ? TEIL ? Theorie ? VEKTOR ? Transduktion ? VERARBEITUNG ? VERFUEGBAR ? VORSCHLAEGE ? DAUER ? UMGANG ? ANALYTISCH ?
License: cc-by-nc-nd/4.0
Language: Deutsch
Time ranges: 2006-10-01 - 2011-07-31
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