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

本数据集源自MIKE 21模型，旨在用于稀疏高斯过程（Sparse GP）模型的训练与验证。数据集旨在用于《通过空间分析和高斯过程学习提升低保真洪水淹没水动力模型技能》一文的发表，其中Chowilla洪泛区作为案例研究。数据集结构分为三个文件夹：

- 原始数据文件夹包含运行水动力模型的结果，包括高保真模型（HF）和低保真模型（LF）的文件夹，两个文件夹均包含MIKE 21 .dfsu数据文件。

- 管理数据文件夹进一步分为三个子文件夹。'Classification_Figures'子文件夹包含为出版物生成的图表，'Events_data'子文件夹包含以二进制格式存储的MIKE 21数据（.npz文件），可通过Python中的Numpy包读取。'SPGP_class_models'子文件夹包含训练好的稀疏高斯过程（SPGP）模型、EOF分析数据和基于单元格数据二进制状态的分类。

- 边界数据文件夹包含水动力模型边界的相关数据，该数据来自气象局在线水数据平台：http://www.bom.gov.au/waterdata/

Python代码位于主文件夹以及https://github.com/nfraehr/Hybrid_LSG_model上。