Publikationsserver der Universitätsbibliothek Marburg
Titel:Data-driven model development in environmental geography - Methodological advancements and scientific applications
Autor:Meyer, Hanna
Weitere Beteiligte: Nauß, Thomas (Prof. Dr.)
Veröffentlicht:2018
URI:https://archiv.ub.uni-marburg.de/diss/z2018/0082
DOI: https://doi.org/10.17192/z2018.0082
URN: urn:nbn:de:hebis:04-z2018-00820
DDC:550 Geowissenschaften
Titel (trans.):Data-driven model development in environmental geography - Methodological advancements and scientific applications
Publikationsdatum:2018-10-15
Lizenz:https://rightsstatements.org/vocab/InC-NC/1.0/

Dokument

Schlagwörter:
Maschinelles Lernen, Umweltmonitoring

Zusammenfassung:
Die Erfassung räumlich kontinuierlicher Daten und raum-zeitlicher Dynamiken ist ein Forschungsschwerpunkt der Umweltgeographie. Zu diesem Ziel sind Modellierungsmethoden erforderlich, die es ermöglichen, aus limitierten Felddaten raum-zeitliche Aussagen abzuleiten. Die Komplexität von Umweltsystemen erfordert dabei die Verwendung von Modellierungsstrategien, die es erlauben, beliebige Zusammenhänge zwischen einer Vielzahl potentieller Prädiktoren zu berücksichtigen. Diese Anforderung verlangt nach einem Paradigmenwechsel von der parametrischen hin zu einer nicht-parametrischen, datengetriebenen Modellentwicklung, was zusätzlich durch die zunehmende Verfügbarkeit von Geodaten verstärkt wird. In diesem Zusammenhang haben sich maschinelle Lernverfahren als ein wichtiges Werkzeug erwiesen, um Muster in nicht-linearen und komplexen Systemen zu erfassen. Durch die wachsende Popularität maschineller Lernverfahren in wissenschaftlichen Zeitschriften und die Entwicklung komfortabler Softwarepakete wird zunehmend der Fehleindruck einer einfachen Anwendbarkeit erzeugt. Dem gegenüber steht jedoch eine Komplexität, die im Detail nur durch eine umfassende Methodenkompetenz kontrolliert werden kann. Diese Problematik gilt insbesondere für Geodaten, die besondere Merkmale wie vor allem räumliche Abhängigkeit aufweisen, womit sie sich von "gewöhnlichen" Daten abheben, was jedoch in maschinellen Lernanwendungen bisher weitestgehend ignoriert wird. Die vorliegende Arbeit beschäftigt sich mit dem Potenzial und der Sensitivität des maschinellen Lernens in der Umweltgeographie. In diesem Zusammenhang wurde eine Reihe von maschinellen Lernanwendungen in einem breiten Spektrum der Umweltgeographie veröffentlicht. Die einzelnen Beiträge stehen unter der übergeordneten Hypothese, dass datengetriebene Modellierungsstrategien nur dann zu einem Informationsgewinn und zu robusten raum-zeitlichen Ergebnissen führen, wenn die Merkmale von geographischen Daten berücksichtigt werden. Neben diesem übergeordneten methodischen Fokus zielt jede Anwendung darauf ab, durch adäquat angewandte Methoden neue fachliche Erkenntnisse in ihrem jeweiligen Forschungsgebiet zu liefern. Im Rahmen der Arbeit wurde eine Vielzahl relevanter Umweltmonitoring-Produkte entwickelt. Die Ergebnisse verdeutlichen, dass sowohl hohe fachwissenschaftliche als auch methodische Kenntnisse unverzichtbar sind, um den Bereich der datengetriebenen Umweltgeographie voranzutreiben. Die Arbeit demonstriert erstmals die Relevanz räumlicher Überfittung in geographischen Lernanwendungen und legt ihre Auswirkungen auf die Modellergebnisse dar. Um diesem Problem entgegenzuwirken, wird eine neue, an Geodaten angepasste Methode zur Modellentwicklung entwickelt, wodurch deutlich verbesserte Ergebnisse erzielt werden können. Diese Arbeit ist abschließend als Appell zu verstehen, über die Standardanwendungen der maschinellen Lernverfahren hinauszudenken, da sie beweist, dass die Anwendung von Standardverfahren auf Geodaten zu starker Überfittung und Fehlinterpretation der Ergebnisse führt. Erst wenn Eigenschaften von geographischen Daten berücksichtigt werden, bietet das maschinelle Lernen ein leistungsstarkes Werkzeug, um wissenschaftlich verlässliche Ergebnisse für die Umweltgeographie zu liefern.

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