forest-fire-area-prediction and forest-fire-prediction
These are ecosystem siblings—both are independent machine learning implementations addressing the same problem domain (forest fire prediction) using different geographic datasets (Portugal vs. Algeria) and potentially different feature engineering approaches, allowing practitioners to compare methodologies rather than choose one over the other.
About forest-fire-area-prediction
UBC-MDS/forest-fire-area-prediction
This project aims to predict the burned area of forest fires in the northeast region of Portugal, using meteorological and soil moisture data.
About forest-fire-prediction
aravind-selvam/forest-fire-prediction
Project for Predicting Algerian Forest Fires and Fire Weather Index Using Machine Learning with Python.
Implements dual predictive models—binary classification (fire/no-fire) and regression (Fire Weather Index)—trained on scikit-learn algorithms including Random Forest, XGBoost, and SVR, with hyperparameter tuning via stratified k-fold cross-validation. Persists Algerian forest fire observations to MongoDB Atlas and exposes predictions through a Flask REST API deployed on Heroku with both web interface and Postman-testable endpoints.
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