wildfire risk forecasting using climax

[moonlanderr]

<insert pull request description here>

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Checklist

Please go through each entry in the below checklist and mark an 'X' if that condition has been met. Every entry should be marked with an 'X' to be get the Pull Request approved.

• All imports are in the first cell?
  • First block of imports are standard libraries
  • Second block are 3rd party libraries
  • Third block are all arcgis imports? Note that in some cases, for samples, it is a good idea to keep the imports next to where they are used, particularly for uncommonly used features that we want to highlight.
• All GIS object instantiations are one of the following?
  • gis = GIS()
  • gis = GIS('home') or gis = GIS('pro')
  • gis = GIS(profile="your_online_portal")
  • gis = GIS(profile="your_enterprise_portal")
• If this notebook requires setup or teardown, did you add the appropriate code to ./misc/setup.py and/or ./misc/teardown.py?
• If this notebook references any portal items that need to be staged on AGOL/Python API playground, did you coordinate with a Python API team member to stage the item the correct way with the api_data_owner user?
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• Code simplified & split out across multiple cells, useful comments?
• Consistent voice/tense/narrative style? Thoroughly checked for typos?
• All images used like <img src="base64str_here"> instead of <img src="https://some.url">? All map widgets contain a static image preview? (Call mapview_inst.take_screenshot() to do so)
• All file paths are constructed in an OS-agnostic fashion with os.path.join()? (Instead of r"\foo\bar", os.path.join(os.path.sep, "foo", "bar"), etc.)
• Is your code formatted using Jupyter Black? You can use Jupyter Black to format your code in the notebook.
• If this notebook showcases deep learning capabilities, please go through the following checklist:
  • Are the inputs required for Export Training Data Using Deep Learning tool published on geosaurus org (api data owner account) and added in the notebook using gis.content.get function?
  • Is training data zipped and published as Image Collection? Note: Whole folder is zipped with name same as the notebook name.
  • Are the inputs required for model inferencing published on geosaurus org (api data owner account) and added in the notebook using gis.content.get function? Note: This includes providing test raster and trained model.
  • Are the inferenced results displayed using a webmap widget?
• IF YOU WANT THIS SAMPLE TO BE DISPLAYED ON THE DEVELOPERS.ARCGIS.COM WEBSITE, ping @jyaistMap so he can add it to the list for the next deploy.

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[priyankatuteja]

expected only 1 file change whereas there are 13 file changes

[moonlanderr]

This is the new pr for this notebook - #2226

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priyankatuteja commented on 2025-02-20T07:21:47Z
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Wildfire risk forecasting across the usa using ClimaX and multi-dimensional time-series weather data

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priyankatuteja commented on 2025-02-20T07:21:48Z
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text is not consistent, some are canel cases while others are not.

Keep minimum headings for example

• Prerequisites
• Introduction
• Necessary Imports
• Visualize training data
• Train a model
• Detect and visualize results in validation set
• Model inference in ArcGIS Pro
• Conclusion
• References

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priyankatuteja commented on 2025-02-20T07:21:49Z
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Wildfires are an increasingly prevalent and destructive natural disaster, particularly in regions of the United States that are prone to dry conditions, high temperatures, and droughts. The unpredictability of wildfires poses significant challenges for disaster management, risk assessment, and environmental protection. Accurate forecasting of wildfire likelihood is crucial for enabling early intervention and effective resource allocation in affected regions.

This project aims to leverage cutting-edge technologies in machine learning and climate data analysis to forecast the likelihood of wildfires in the United States. Specifically, it utilizes the pretrained ClimaX foundational model—a state-of-the-art machine learning model by Microsoft, designed for forecasting weather and climate tasks as well as events impacted by them, such as wildfires. Here, the model utilizes a multi-dimensional time-series dataset**,** the Wildfire Weather Cube. This dataset contains historical data on key weather parameters such as temperature, humidity, wind speed, and precipitation (remove 'etc.') recorded from 2001 to 2021, which are critical in predicting the conditions conducive to wildfires.

The project intends to generate a likelihood raster of wildfire risk, enabling more informed decision-making for emergency response teams, policymakers (one word), and communities. The objective is to provide actionable insights into when and where such events are most likely to occur, based on historical weather patterns and climate projections.

Key Corrections:

1. "for enabling" – Grammatically correct, but "to enable" would also work.
2. "as well as" instead of "and" for clarity.
3. Comma added after 'Here' for proper sentence structure.
4. "historical" instead of "historic" (historic refers to something historically significant).
5. Removed 'etc.' – It's redundant since the list is already broad.
6. Corrected "recorded from the 2001 to 2021" → "recorded from 2001 to 2021" (remove "the").
7. "Policymakers" is one word, not two.

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priyankatuteja commented on 2025-02-20T07:21:49Z
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All non-arcgis imports should be on top, followd by arcgis related imports. Currently, it's a mix of both

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priyankatuteja commented on 2025-02-20T07:21:50Z
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we should remove this cell

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priyankatuteja commented on 2025-02-20T07:21:51Z
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_out_variables is a hidden argument which should not be exposed

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priyankatuteja commented on 2025-02-20T07:21:52Z
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heading not required

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priyankatuteja commented on 2025-02-20T07:21:52Z
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make spacing consistent

model = ClimaX(data, backbone="1.40625deg", patch_size=4)

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priyankatuteja commented on 2025-02-20T07:21:53Z
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We should have model.supported_backbones propery, however the one used here is a hidden one which should not be used

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priyankatuteja commented on 2025-02-20T07:21:54Z
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spacing should be fixed

odel.save("climax_wildfire_likelihood_forecast_v1.model", publish=True)

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priyankatuteja commented on 2025-02-20T07:21:55Z
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incorrect formatting. the code is visible for pasting gif