Hybrid Machine Learning Methods for the Reconstruction and Disaggregation of Gridded Climatic and Hydrological Variables – Ujjwal Singh
Ujjwal Singh
Doctoral thesis
Hybrid Machine Learning Methods for the Reconstruction and Disaggregation of Gridded Climatic and Hydrological Variables
Abstract:
Ongoing climate change examining scenarios, accurate precipitation estimation and understanding runoff dynamics are vital in managing hydrology, meteorology, and water resources. While different in focus, these two domains contribute to an intricate understanding of water cycle components, particularly in regions experiencing water resource degradation. In both studies presented within this doctoral …more
Language used: English
Date on which the thesis was submitted / produced: 21. 2. 2024
Thesis defence
- Supervisor: doc. Ing. Petr Máca, Ph.D.
- Reader: Adam Vizina, externi, Martina Zeleňáková, externi, Golan Bel, externi
Citation record
ISO 690-compliant citation record:
SINGH, Ujjwal. \textit{Hybrid Machine Learning Methods for the Reconstruction and Disaggregation of Gridded Climatic and Hydrological Variables}. Online. Doctoral theses, Dissertations. Praha: Czech University of Life Sciences Prague, Faculty of Environmental Sciences. 2024. Available from: https://theses.cz/id/5573ny/.
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Published in Theses:- světu
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Institution archiving the thesis and making it accessible: Česká zemědělská univerzita v Praze, Fakulta životního prostředíCzech University of Life Sciences Prague
Faculty of Environmental SciencesDoctoral programme / field:
Landscape Engineering / Environmental Modelling
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