Artificial Neural Networks for Knowledge Extraction in Spatiotemporal Dynamics and Weather Forecasting

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Zitierfähiger Link (URI): http://hdl.handle.net/10900/156815
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1568159
http://dx.doi.org/10.15496/publikation-98147
Dokumentart: Dissertation
Erscheinungsdatum: 2024
Sprache: Englisch
Fakultät: 7 Mathematisch-Naturwissenschaftliche Fakultät
Fachbereich: Informatik
Gutachter: Butz, Martin
Tag der mündl. Prüfung: 2024-03-22
DDC-Klassifikation: 004 - Informatik
Schlagworte: Deep learning , Wetter , Wettervorhersage , Maschinelles Lernen , Neuronales Netz
Freie Schlagwörter:
spatiotemporal dynamics
knowledge extraction
physics aware neural networks
deep learning
weather prediction
deep learning weather prediction
Lizenz: https://creativecommons.org/licenses/by/4.0/deed.de
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Abstract:

This thesis explores the potential of machine learning methods for improving weather forecasts. Since weather is considered a spatiotemporal process that evolves over space through time, the thesis first investigates the design choices required for machine learning models to simulate synthetic spatiotemporal processes, such as the two-dimensional wave equation. It then develops a method for analyzing machine learning models that enables the extraction of unknown process-relevant context that parameterizes an observed simulated spatiotemporal process of interest. Relating these extracted factors to physical properties leads the thesis to physics-aware machine learning, where it explores how to fuse process knowledge from physics with the learning ability of artificial neural networks. Given the insights from those investigations, a competitive deep learning weather prediction model is designed to understand which design choices support data-driven algorithms to learn a meaningful function that predicts realistic and stable states of the atmosphere over hundreds of hours, days, and weeks into the future.

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