Starts: June 16, 2022, at 6 pm ET, and continues weekly [$2500 in gifts for participants]
We will be applying time series and other relevant techniques to Canadian streamflow data in drought-prone areas.
Pre-Requisites - Prior experience using Python is required for this project. We recommend that participants have heard of time series, neural networks, Google Colab, and data cleaning, so they can perform ML-related modelling tasks. Knowledge of environmental science/engineering is not required, but an interest is preferred.
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Leads / Advisors
~ Yan Nusinovich | Data Scientist at Slate.AI
~ Dr Andre Erler | Advisor
Senior Climate Scientist Aquanty
~ Dr. Karen Smith | Advisor
Assistant Professor, U of Toronto
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â Meeting link: https://meet.jit.si/canadian_prairies
â Weekly meeting time: Thursdays, 6pm ET
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Overview
- âAdequate water resources management is an essential component of socioeconomic security and development. This is made even more critical with the increasing global population and impacts of climate change on water resourcesâ (https://www.nature.com/articles/s41597-020-00583-2).
- âA long-standing problem in the hydrological sciences is about how to use one model, or one set of models, to provide spatially continuous hydrological simulations across large areas (e.g., regional, continental, global)â (https://hess.copernicus.org/articles/23/5089/2019/hess-23-5089-2019.html with accompanying code at https://github.com/kratzert/ealstm_regional_modeling).
- Minimal goal: create a time series model for predicting streamflow using Natural Resources Canada data.
- Stretch goal: create a model that automatically updates streamflow predictions at regular intervals.
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Tentative Timeline
# | Major Milestones | Expected time to finish | Learning Recipes/Recordings |
1 | Discuss the problem + ways to approach | 2 weeks | Streamflow Modelling with Time Series
https://ai.science/l/9d5a8e1a-e2ab-407c-ada5-371cfa0a3c89
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Data for Streamflow Prediction
https://ai.science/l/a30e3c41-fb68-4755-9aa1-9986b4dbc564
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Learning Multivariate Time Series
https://ai.science/l/50144be7-9b81-443a-b67b-ab81ab5516d2
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Meeting 2 recording: https://www.dropbox.com/s/fne2dyh78qtzaiz/canadian_prairies on 2022-06-23 23-05.mp4?dl=0 |
2 | Members present how they will approach + Core Team Distilled | 1 week | |
3 | Project period | 4 - 6 weeks | Meeting 4 recording:
https://www.dropbox.com/s/cfd2a09howhtejr/canadian_prairies on 2022-07-07 23-07.mp4?dl=0
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Meeting 5 recording:
https://www.dropbox.com/s/c93w54raszvr6po/canadian_prairies on 2022-07-14 23-14.mp4?dl=0
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Meeting 6 recording:
https://www.dropbox.com/s/7ziaqmfev0520z4/canadian_prairies on 2022-07-21 23-02.mp4?dl=0 |
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What will I have to show at the end of this project?
- Contributions to the projectâs GitHub repo
- A highlight in the project spotlight page on the Aggregate Intellect community website.
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Prep Steps
- Bookmark the Working Groupâs landing page. All key info centralized there.
- Join our Working Groupâs Slack channel by:
- By clicking here.
- If having trouble joining, drop âammar@ai.scienceâ an email. Heâll add!
- [STRONGLY RECOMMENDED BUT OPT] Virtual environment - Google Colab.
- Google Colab is essentially a Google Cloud hosted Juypter notebook.
- Synchronizes everyoneâs environments for easier collaborative bug fixing.
- Gives free GPU access. Google Colab - How to use it
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Working Group Journey [more info is being added as we go]
Session #1 + #2:
- Get to know everyone!
- We will introduce the problem & why it matters.
- Discuss how weâre thinking about the solution.
- Review available data.
- Review possible modelling approaches.
- Think about how we can reach (or modify, if needed) our stretch goal.
Session #3:
- Present your approach and ask any clarifying questions.
- End of session â Apply to join the Project Team if itâs your cup of tea.
- Energy & competency are both important, but energy is a little more.
Session #4+ [Tentative length: 1.5 months]:
- Project Team will work towards the core problem.
- Will be recognized by AISC at the conclusion of the project!
- [For your portfolio / blog / Social Media].
At conclusion:
- Fill out a form reporting contributions of your colleagues to help divvy up the $2500 in gifts.
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Why join?
Aggregate Intellect hosts one of the most diverse ML communities in the world. Over the course of the working group:
- Youâll get an immersion into that community & walk out with some cool new friends.
- There are $2500 in gifts budgeted for participants based on contribution. (A nice little push to encourage đ)
- Youâll gain new skills that can make an impact on the worldâs most pressing challenges.
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To join in, join in the Slack channel by hitting âjoinâ, drop a quick intro & mention youâre interested in being a part of the group. Someone will help!
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