This repository contains the IPython notebooks used for the analysis presented in our paper on flood prediction using the NWM-CNN model. The analysis demonstrates the capability of the NWM-CNN model to predict surface water area across California, leveraging data from the U.S. National Water Model and a convolutional neural network.
- Figure1.ipynb: Analysis code for Figure 1 in the paper.
- Table1.ipynb: Analysis code for Table 1 in the paper.
- Figure2: QGIS map (no code), which can be found in the Hydroshare resource described below.
- Figure3.ipynb: Analysis code for Figure 3 in the paper.
- environment.yml: Conda environment file to recreate the analysis environment.
- The data used in this analysis is hosted on HydroShare (https://www.hydroshare.org/resource/8b76906c4b604c458fbcb5ea7c8c0be7) and has the following directory structure within the repository:
- NWM-CNN_predictions: Predictions from the NWM-CNN model.
- csv_files: Miscellaneous data files in CSV format.
- images_for_sacramento_stats: Images and statistics for Sacramento area analysis.
- map: QGIS map files.
- shapefile: Shapefiles used in the analysis.
To use the analysis notebooks:
- Ensure you have Conda installed.
- Clone this repository to your local machine.
- Navigate to the repository directory
- Download the data from the HyroShare link above.
wget https://www.hydroshare.org/resource/8b76906c4b604c458fbcb5ea7c8c0be7/data/contents/data.zip- Unzip the compressed file.
unizp data.zip
- create the Conda environment from environment.yml:
conda env create -f environment.yml
- Activate the environment:
conda activate nwm-cnn
- Open the Jupyter notebooks in Jupyter Lab or Notebook
- Modify the paths to your locally downloaded data, replacing the existing path if needed:
LOC_DATA_DIR = "./data/"
For more information please visit: (https://www.floodbase.com/about)