https://github.com/acl2171/wildfire_model

Can we apply machine learning techniques to predict where wildfires are most likely to spread? This project explores a subset of that question: for one point in time (December 22, 2019), can we use weather data to identify active fires, burned areas, land (other), and water in Australia?
https://github.com/acl2171/wildfire_model

Last synced: 11 days ago
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Can we apply machine learning techniques to predict where wildfires are most likely to spread? This project explores a subset of that question: for one point in time (December 22, 2019), can we use weather data to identify active fires, burned areas, land (other), and water in Australia?

• Host: GitHub
• URL: https://github.com/acl2171/wildfire_model
• Owner: acl2171
• Created: 2020-01-07T15:33:47.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-10T16:43:11.000Z (almost 5 years ago)
• Last Synced: 2024-08-02T15:06:07.158Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 24.7 MB
• Stars: 4
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Identifying Wildfires: Geospatial Data and Machine Learning

Allison Lee

Blog Post 

This project was created over the course of 2.5 weeks as part of the Flatiron School Data Science Fellowship in DC. This project is part of a longer-term goal to explore whether we can use machine learning approaches to predict the spread of wildfires. 

**--Project Status: [Active]**

## Project Goal

Can we apply machine learning techniques to predict where wildfires are most likely to spread? This project explores a subset of that question: for one point in time (December 22, 2019), can we use weather data to identify active fires, burned areas, land (other), and water in Australia? 

    

## Technologies

    

 - Python

 - Google Earth Engine

 - Google Cloud Platform

 - Rasterio

 - Xarray

 - Geopandas

 - Pandas

 - Numpy

 - Sci-kit Learn

 - Scipy

 - Pyarrow

 - IMBLearn

 - Joblib

 - Matplotlib

 - Tableau

 - Yellowbrick

 - Git

 - Jupyter Lab

    

## Links to Files

 - Slidedeck (PDF)

 - Data Sources (accessed via Google Earth Engine)

    - MCD64A1.006 MODIS Burned Area Monthly Global 500m (Land Processes Distributed Active Archive Center (LP-DAAC)  within NASA’s Earth 

        Observing System Data and Information System)

    - MOD14A1.006: Terra Thermal Anomalies & Fire Daily Global 1km (Land Processes Distributed Active Archive Center (LP-DAAC)  within 

        NASA’s Earth Observing System Data and Information System)

    - GSMaP Operational: Global Satellite Mapping of Precipitation (Earth Observation Research Center, Japan Aerospace Exploration Agency)

    - Global Land Data Assimilation System (GLDAS 2.1) (NASA’s Goddard Earth Sciences Data and Information Services Center)

 - Notebooks

    - Master Notebook

    - Data Collection

    - Data Cleaning

    - Modeling

 - Python Files

    - Data Cleaning

    - Modeling

    

## Contact   

    

Feel free to reach out at [email protected] if you have questions.