Data from MIKE 21 models for training and validation of Sparse GP models in "Upskilling low-fidelity hydrodynamic models of flood inundation through spatial analysis and Gaussian Process learning"

Data from MIKE 21 models for training and validation of Sparse GP models.

The data is used for publication in "Upskilling low-fidelity hydrodynamic models of flood inundation through spatial analysis and Gaussian Process learning" with the Chowilla floodplain as case study.

The data is structured in three folders:

- The raw data folder contains results for running the hydrodynamic models. One folder for the high-fidelity model (HF) and one for the low-fidelity model (LF). Both folders contain MIKE 21 .dfsu data files.

- The managed data folder is structured in three folders. "Classification_Figures" contain figures generated for the publication. "Events_data" contains the MIKE 21 data in binary format as .npz files to be read via the Numpy package in Python. "SPGP_class_models" contains the trained Sparse GP (SPGP) models, EOF analysis data and categories depending on the binary state of the data on cell level.

- Boundary data folder contain data for the boundaries of the hydrodynamic models. This data is retrieved from the Bureau of Meteorology's online water data platform: http://www.bom.gov.au/waterdata/

Python code is located in the main folder and on https://github.com/nfraehr/Hybrid_LSG_model