My First Machine Learning Project: Predicting Forest Fires

Embarking on the world of machine learning was a pivotal step in my journey toward becoming a data-driven researcher. My first project, Forest Fire Prediction, focused on a critical real-world problem: predicting the burned areas of forests based on weather and environmental data. The goal was to create a model that could help identify potential forest fire risks by analyzing features such as temperature, wind speed, humidity, and specific fire weather indices.

Project Background

Forest fires pose significant environmental challenges, from destroying ecosystems to contributing to climate change. Predicting the area likely to be burned during a forest fire is complex due to the intricate interactions between multiple environmental factors. For this project, I used a dataset from the Montesinho Park in Portugal, which includes features like:

• Spatial Coordinates (X, Y): Representing the location of the fire.

• Weather Features: Temperature, wind speed, humidity, and rain.

• Fire Indices:

  • FFMC (Fine Fuel Moisture Code): Measures the moisture content of forest litter and fine fuels.

  • DMC (Duff Moisture Code): Indicates the moisture content of decomposing organic matter.

  • DC (Drought Code): Reflects the dryness of compact organic layers.

  • ISI (Initial Spread Index): Combines wind and fuel moisture for predicting fire spread.

The target variable was the burned area, representing the hectares of forest affected by fire.

Model Selection

For this project, I applied three machine learning models:

1. Linear Regression

2. Random Forest Regressor

3. Gradient Boosting Regressor

Each model was chosen to explore different approaches, from simple linear relationships to more complex decision-tree-based methods. I also experimented with a logarithmic transformation of the target variable to address the heavy skewness of the burned area data, where most observations were small fires (near zero hectares).

Challenges Encountered

As expected, predicting forest fire areas was no easy task. The key challenges I faced included:

1. Skewed Data: The burned area variable was highly imbalanced, with most fires affecting very small areas. This made it difficult for models to capture patterns and predict larger fires.

2. Non-linear Relationships: Many of the relationships between weather features, fire indices, and the burned area were complex, and simple models like linear regression struggled to accurately model them.

3. Overfitting: More complex models like Random Forest and Gradient Boosting risked overfitting to the training data, making them less effective when predicting on unseen data.

Despite applying hyperparameter tuning, the models' performance was suboptimal, with negative R² values indicating that they performed worse than a simple mean prediction.

Key Takeaways

One of the most important lessons from this project was the need to continuously refine models and apply advanced techniques to handle challenges such as data imbalance and non-linearity. The journey didn't end with perfect predictions, but it has been an invaluable learning experience. This project opened my eyes to the complexity of environmental data and the exciting potential of machine learning to tackle real-world issues.

Future Steps

I am still learning, and I plan to improve this project by:

• Exploring More Advanced Models: I aim to apply more robust algorithms such as XGBoost or LightGBM, which have been shown to work well on structured data.

• Feature Engineering: Creating new features or interaction terms between variables might help capture hidden patterns that current models missed.

• Handling Data Imbalance: I will explore techniques like resampling or cost-sensitive learning to deal with the skewed distribution of the burned area variable.

• Model Evaluation: Beyond R² and MSE, I want to explore other metrics or even probabilistic models that can give better insights into fire prediction.

  
  

Conclusion

My first foray into machine learning has been both exciting and challenging. The forest fire prediction project has taught me how to manage datasets, apply different algorithms, and interpret model results. Although my initial models were far from perfect, they represent a strong foundation for future work. With each new project, I continue to grow and push the boundaries of what’s possible using machine learning.

This is just the beginning of my journey into data science, and I look forward to improving these models as I expand my knowledge and skills!

Thanks for reading, and stay tuned for more updates on my projects!