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https://github.com/sralter/classifire

Wildfire Prediction Model: Samuel Alter's BrainStation 2023 Data Science Capstone Project
https://github.com/sralter/classifire

qgis scikit-learn tensorflow

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Wildfire Prediction Model: Samuel Alter's BrainStation 2023 Data Science Capstone Project

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README 

        
# Classi**FIRE**

_Samuel Alter's BrainStation 2023 Data Science Capstone Project, Spring 2023_



## Motivation

- Wildfires, or the uncontrolled burning of vegetation, cause enormous damage and loss of life worldwide every year.[^1]

- **33 people died** and **>4.3 million acres burned** in California in 2020 alone. This is **equivalent to the total combined area of Puerto Rico and Rhode Island**.

- Although they are a natural component of some forest ecosystems, **wildfire incidents are projected to increase** in our warming world.[^2]

- While society transitions to a greener future, **there is a clear need to predict where wildfires are likely to occur** so that communities can protect themselves and evacuate when necessary.



## Introduction

- For this project, I collected a combination of **satellite imagery**, **topographic data** (aspect, elevation, and slope), and **historical fire boundaries** to train models that would predict wildfire risk (**Figure 1**). 

- I first used [QGIS](https://qgis.org/en/site/), an open-source mapping application, to setup the relationships between the datasets.

- Then used **Python** to process and **model the data**.

- I fed the processed data into machine learning and deep learning models to make my predictions.

- I chose to focus on the **Santa Monica Mountains in Ventura and Los Angeles Counties**, for its relatively manageable **size**, **proximity to populated areas**, and its **east-west trend**, which makes the topographic analysis easier.



Figure 1: Data ingestion pipeline



## Datasets Used

- **Satellite imagery** and **topographic data** from USGS’ EarthExplorer[^3]

- Historical records of **wildfire burn boundaries** from the National Interagency Fire Center[^4]

- File formats used: .tif and .jpg (imagery); .geojson and .csv (historical wildfire data).



## Mapping, Data Cleaning, and Preprocessing

- In order to train a model that integrates spatial information with historical fire boundaries, I had to find a way to quantify the continuous spatial information. I settled on **building an evenly-spaced points layer** that would have the topographic and fire boundary data from under that point appended to the file (**Figure 2**).

- Since the satellite photos were from 2018, I removed all fire polygons from after 2018 and **merged the rest to one shape**.

- Using the point layer, **an overlapping function could determine if a part of the landscape experienced a fire (“fire” area) or not (“nofire” area)**. The resultant file would serve as the “topographic” data in the project.

- For the geographic data, I focused on **aspect** (i.e. what direction the land is facing), **elevation** (meters above sea level), and **slope** (degrees). It is a straightforward operation to extract these data from an elevation raster and append them to the point layer.

- I **tiled the satellite imagery into 128x128 pixels** that would be fed into a **Tensorflow image model**, and saved them into two folders (i.e., fire and nofire).

- At the end of the data collection step, I had a **table of geographic data** and **two image folders** (fire areas and non-fire areas).



Figure 2: Mapping flow from QGIS to modeling



## Modeling, Results, and Insights

- Since data is in two forms (point-based topographic information and satellite image tiles), I trained two models. After finding an optimal model, I created a metamodel, which used the predictions from the topographic and image analysis models as factors for a new logistic regression (**Figure 3**).



Figure 3: Modeling pipelines using statsmodels, sklearn, Tensorflow (VGG19), and finally a metamodel with sklearn's logistic regression.



  - _Topographic Data_  

    - The **areas affected by fire** were predominantly **located in mountainous regions with higher mean elevation** compared to fire-free areas (**Table 1**).

    - Correlations were observed between different topographic factors, such as categorical aspect with continuous aspect, and categorical slope and elevation with continuous elevation and slope.

    - `LogisticRegression` **was chosen as the modeling approach** for classifying fire incidence based on topographic data, revealing the relative importance of different features.

    - Various models were tested, including `naive_bayes`, `Bernoulli` and `Gauss`, `XGBClassifier`, `RandomForestClassifier`, `AdaBoost`, and `GradientBoostingClassifier`, with `GradientBoostingClassifier` **achieving the highest accuracy**.

    - Grid search was employed to optimize the GradientBoostingClassifier model, but the best accuracy remained the same with specific parameter values.



Table 1: Summary statistics by fire/nofire areas



  -  _Satellite Imagery_  

     -  BigEarthNet[^5] suggested the **pre-trained VGG19 model** to be sufficient, so I used that with **Tensorflow-Keras** and **added a final dense layer with two output nodes** to represent the fire/nofire categories required by this project.

     -  With **20,025,410 total parameters**, I deemed the model more than sufficient for the project’s needs.

     -  After training, **the model achieved an accuracy of 95.6%** on classifying all the images.

     -  **Figure 4** shows typical images in the set as well as the locations of the two areas (fire and non-fire) that I used to feed the models.



Figure 4: examples of fire and nofire satellite images

Figure 3: Map of the sections within the study area used to feed the models



  -  _Metamodel_

     -  To construct the metamodel, I **extracted the predictions from the topographic and imagery datasets, used them as features**, and ran the two through a **scikit-learn `LogisticRegression`, which achieved over 99% accuracy**. This concluded the modeling portion of the project.



## Findings and Conclusions

- The statsmodels `Logit` model reveals that **higher elevations and west- and north-facing hillslopes correlate with wildfires, potentially due to dominant wind directions in the area**.

- The project serves as a **proof-of-concept**, demonstrating the capabilities of a machine-learning model using remotely-sensed data for predicting wildfire risk. **Future iterations can expand by incorporating weather and time series analysis** and **testing the model on different landscapes**.

- Despite limitations, such as lack of high-resolution weather data, **the project shows promise for developing a robust wildfire risk prediction model** with broader data and automated GIS workflows.

- **Publishing the model in a more accessible manner** could provide communities with **valuable insights into their wildfire risk**.



## Postscript: Commentary on why the accuracy is so high

* Accuracy is almost 100%, which is suspiciously high

* Since the model was trained on either 100% burned areas or 100% unburned areas, it only knows the clearly-delineated cases.

* Furthermore, when tested, the model was only given 100% burned or unburned areas

* The unburned area was a city center. There would never be a wildfire in a concrete jungle

* The opposite is similarly always true: in the mountains, away from all civilization, fires are extremely likely and are much harder to combat



## Extra goodies

- Correlation matrix of the geographic data:  

Correlation matrix of the geographic data



- Summary of models and results for TopoData:  

Summary of models and results for TopoData



- Summary of satellite imagery model:  

Summary of satellite imagery model



- Summary of metamodel:  

Summary of metamodel



- Satellite image and historic wildfire boundaries (up to 2018):  

Satellite image and historic wildfire boundaries (up to 2018)



- Satellite image and hillshade of study area:  

Satellite image and hillshade of study area



[^1]: [https://www.fire.ca.gov/incidents/2020/]

[^2]: [https://report.ipcc.ch/ar6syr/pdf/IPCC_AR6_SYR_SPM.pdf]

[^3]: [https://earthexplorer.usgs.gov]

[^4]: [https://data-nifc.opendata.arcgis.com/]

[^5]: [https://bigearth.net/]