Deep learning and hybrid modeling of global vegetation and hydrology

Kraft, Basil

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Deep learning and hybrid modeling of global vegetation and hydrology

Kraft, B. (2022). Deep learning and hybrid modeling of global vegetation and hydrology. PhD Thesis, Technical University of Munich, München.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-C8AE-1 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-7A8D-C

Genre: Thesis

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http://mediatum.ub.tum.de/doc/1638184/document.pdf (Postprint) Open Access Green

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Thesis (hosted by TU Munich)

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Green

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Creators:
Kraft, Basil^{1, 2}, Author

Affiliations:
1Global Diagnostic Modelling, Dr. Martin Jung, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1938311
2IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society, Hans-Knöll-Str. 10, 07745 Jena, DE, ou_1497757

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Dates: Date issued: 2022

Publication Status: Issued

Pages: -

Publishing info: München : Technical University of Munich

Table of Contents: -

Rev. Type: -

Identifiers: Other: bgc312

Degree: PhD

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