Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction.

Area of Science:

• Environmental Science
• Computer Science
• Ecosystem Management

Background:

• Wildfires significantly impact ecosystems and require timely intervention strategies.
• Existing wildfire forecasting relies on weather data, but advanced methods using comprehensive data offer advantages.
• Predicting wildfire occurrence is complex due to diverse environmental and geographical factors, lacking a unified approach.

Purpose of the Study:

• To explore the potential of machine learning (ML) algorithms for wildfire occurrence forecasting.
• To develop a unified pipeline for data acquisition and ML model development using diverse environmental parameters.
• To assess the performance of various ML algorithms in predicting wildfire occurrences across different regions.

Main Methods:

• Utilized a comprehensive dataset of over 17,000 wildfire events in central Russia over 10 years.
• Applied a range of ML algorithms including Random Forest, XGBoost, Autoencoder, ConvLSTM, Attention Multilayer Perceptron, and RegNetX.
• Developed a unified data acquisition and ML pipeline, addressing challenges of imbalanced spatio-temporal data.

Main Results:

• Achieved F1-scores ranging from 0.7 to 0.87, indicating significant predictive potential.
• Demonstrated that region-specific ML models, considering local environmental features, yield better accuracy.
• Highlighted the effectiveness of integrating meteorological, geo-spatial, and anthropogenic data for improved forecasting.

Conclusions:

• Tailored ML models are essential for accurate wildfire occurrence prediction in different geographical regions.
• The developed pipeline and tested algorithms show promise for enhancing wildfire management and emergency response.
• Integrating advanced data sources and AI techniques can substantially improve decision-making for wildfire mitigation efforts.