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Applying AI to HEC-RAS Modeling Workflows

Australian Water School via YouTube

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Overview

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Explore the application of AI-powered coding environments to enhance HEC-RAS modeling workflows in this informative webinar. Discover how ChatGPT's code interpreter can streamline data manipulation and analysis traditionally performed in Excel or specialized applications. Learn to leverage AI-generated Python code for HEC-RAS capabilities, including terrain modifications, custom data exports, and GIS script creation. Gain insights into tools like HEC-Commander, RAS-Commander, and DSS-Commander for improved hydraulic modeling. Witness practical examples showcasing gauge station analysis and storm event modeling. Understand the potential of AI coding in local notebooks and the Brunner-Runner tool for optimizing HEC-RAS processes. Conclude with a Q&A session and information on upcoming premium webinars for in-depth demonstrations and hands-on experience with AI applications in hydraulic modeling.

Syllabus

- Presenter intro | AI resources
- HEC-RAS capabilities with AI generated python code
- Example 1 Terrain modifications | Design channels
- Example 2 Custom HEC-RAS data exports
- my HDF5 | Outputs in ChatGPT
- Example 3 GIS script for G&A infiltration layer
- HEC-Commander repository GitHub
- HEC-RAS Python Tools | RAS-Commander | DSS-Commander
- Terrain Modification Profiler
- AI Coding in local notebooks
- Brunner-Runner tool
- Example 1 | Gauge stations | Big Storm 2020
- Example 2 | same script
- Q&A
- Wrap-up | Premium Webinar and live course details

Taught by

Australian Water School

Reviews

5.0 rating, based on 1 Class Central review

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  • Profile image for Joshua Malikebu
    Joshua Malikebu
    1
    to be honest this course was so great to me because it has helped in understanding much on how we can use chatgpt to use in hecras with an intention to come up with the output based on the activity and project you are working on and also introduction of HECRAS Commander will assists alot of the researchers to understand and to reduce the time frame preparing the data for the prediction and other early warning systems that may be preparing toward different events that happens frequently and persistently in their communities and other different societies
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