Predicting Forest Fires Using Machine Learning

Forest fires cause catastrophic damage to ecosystems, wildlife, human lives, and the economy. Predicting forest fires before they spread uncontrollably is crucial for mitigation. Environmental factors such as temperature, humidity, rainfall, and wind speed are complex and non-linear, making manual prediction difficult. Machine learning can analyze historical environmental patterns and fire occurrences to develop predictive models that provide early warnings and help save forests.

By training machine learning models on meteorological data and historical fire incidents, we can predict areas at high risk of fire outbreaks. Models like Random Forests, SVM, Logistic Regression, and Neural Networks can classify risk levels. Integrating satellite data, vegetation indexes (NDVI), and human activity patterns further enhances prediction accuracy, enabling authorities to take preemptive action for firefighting and evacuation planning.

Datasets containing environmental variables like temperature, relative humidity, wind speed, rainfall, and fire occurrence data are collected. Data preprocessing includes cleaning, feature scaling, and balancing classes (fire/no fire). Classification models are trained to predict the probability of fire outbreaks based on current conditions. Risk heatmaps and alert systems can be generated from model outputs to support proactive firefighting efforts.

• Collect environmental datasets (meteorological + past fire records) from sources like NASA FIRMS, UCI datasets, and NOAA.
• Preprocess data: handle missing values, scale features, and encode labels (fire risk: high/low/no risk).
• Train machine learning models like Random Forests, Gradient Boosting, or Deep Learning classifiers on the dataset.
• Evaluate models using metrics like accuracy, recall (important for catching fires early), and ROC-AUC scores.
• Deploy a risk prediction dashboard or heatmap for visualization and early warning communication systems.

• UCI Forest Fire Prediction Dataset
• NASA FIRMS Fire Monitoring
• Geospatial Mapping with Folium Python