SynxFlow
Dynamically simulate flood inundation, landslides runout and debris flows using multiple CUDA-enabled GPUs.
https://github.com/synxflow/synxflow
Category: Climate Change
Sub Category: Natural Hazard and Storm
Keywords
debrisflow flood landslide modelling
Keywords from Contributors
hydrology flood-modelling landslidesimulation
Last synced: about 7 hours ago
JSON representation
Repository metadata
Simulates flood, landslide and debris flow dynamically using GPUs
- Host: GitHub
- URL: https://github.com/synxflow/synxflow
- Owner: SynxFlow
- License: gpl-3.0
- Created: 2023-09-18T21:26:19.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-31T21:15:01.000Z (28 days ago)
- Last Synced: 2025-04-26T01:02:12.596Z (2 days ago)
- Topics: debrisflow, flood, landslide, modelling
- Language: C++
- Homepage: https://synxflow.readthedocs.io
- Size: 5.77 MB
- Stars: 54
- Watchers: 5
- Forks: 7
- Open Issues: 2
- Releases: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Authors: authors.md
README.md
SynxFlow: Synergising High-Performance Hazard Simulation with Data Flow
What the software can do
This software can dynamically simulate flood inundation, landslides runout and debris flows using multiple CUDA-enabled GPUs. It also offers an user-friendly yet versatile Python interface that can be fully integrated into data science workflows, aiming to streamline and accelerate hazard risk assessment tasks.
Using the model
For how to install and use the model, please visit here
Running the model on Google Colab
The tutorials can also run on Google Colab by using one of the following links
Click me to run a flood simulation
Click me to run a landslide runout simulation
Click me to run a debris flow simulation
Using LLMs to control SynxFlow
It is also possible to use Large Language Models (LLMs) to generate script to use SynxFlow and build complex workflows.
Please see this tutorial to learn how to do this.
Acknowledgment
SynxFlow represents our distinct vision for the next generation of tools in this field, aiming to address evolving challenges and user needs with cutting-edge technologies. Our goal is to offer powerful, yet user-friendly tools for research and industrial applications, ensuring broad accessibility and applicability. In this spirit, SynxFlow is committed to being an open-source, community-driven and inclusive project. SynxFlow inherits code from established open-source software such as HiPIMS-CUDA [1] and Pypims [2]. The development of SynxFlow has also benefited from the skills, knowledge, and experience gained by its authors while contributing as main developers to HiPIMS-CUDA and Pypims.
[1] HiPIMS stands for High-Performance Integrated hydrodynamic Modelling System. HiPIMS is an open source flood Modelling suite developed and maintained by Prof Qiuhua Liang and his team in Loughborough University.
[2] Pypims is a further development of HiPIMS-CUDA to provide a Python interface.
Owner metadata
- Name:
- Login: SynxFlow
- Email:
- Kind: user
- Description:
- Website:
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/145569668?v=4
- Repositories: 1
- Last ynced at: 2023-10-04T11:54:55.228Z
- Profile URL: https://github.com/SynxFlow
GitHub Events
Total
- Create event: 2
- Release event: 1
- Issues event: 15
- Watch event: 22
- Issue comment event: 9
- Push event: 18
- Pull request event: 6
- Fork event: 3
Last Year
- Create event: 2
- Release event: 1
- Issues event: 15
- Watch event: 22
- Issue comment event: 9
- Push event: 18
- Pull request event: 6
- Fork event: 3
Committers metadata
Last synced: 8 days ago
Total Commits: 157
Total Committers: 6
Avg Commits per committer: 26.167
Development Distribution Score (DDS): 0.306
Commits in past year: 21
Committers in past year: 2
Avg Commits per committer in past year: 10.5
Development Distribution Score (DDS) in past year: 0.19
| Name | Commits | |
|---|---|---|
| Xilin Xia | x****9@g****m | 109 |
| Xiaodong Ming | x****g@o****m | 32 |
| Xilin Xia | x****2@l****k | 9 |
| thivinanandh | t****h@g****m | 4 |
| SynxFlow | 1****w | 2 |
| pypims | 6****s | 1 |
Committer domains:
- lboro.ac.uk: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 17
Total pull requests: 5
Average time to close issues: 4 days
Average time to close pull requests: 19 days
Total issue authors: 7
Total pull request authors: 3
Average comments per issue: 1.76
Average comments per pull request: 1.2
Merged pull request: 5
Bot issues: 0
Bot pull requests: 0
Past year issues: 14
Past year pull requests: 3
Past year average time to close issues: 5 days
Past year average time to close pull requests: about 1 month
Past year issue authors: 6
Past year pull request authors: 2
Past year average comments per issue: 1.21
Past year average comments per pull request: 0.0
Past year merged pull request: 3
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- zhangrf21 (6)
- jennykupzig (4)
- weiyilan (3)
- barneydobson (1)
- thivinanandh (1)
- Kong-JiaTao (1)
- kinghuihui9999 (1)
Top Pull Request Authors
- thivinanandh (2)
- mingxiaodong (2)
- xiaxilin (1)
Top Issue Labels
Top Pull Request Labels
Dependencies
- fiona *
- imageio *
- matplotlib *
- myst-parser *
- nbsphinx *
- numpy *
- pandas *
- pyshp *
- rasterio *
- scipy *
- sphinx >=4.1
- sphinx-rtd-theme >=1.0.0
- fiona *
- imageio *
- matplotlib *
- numpy ==1.23.5
- pandas ==1.5.3
- pyshp *
- rasterio *
- scipy ==1.10.1
Score: 5.817111159963204