Predictive Modeling of Wildfire Spread

Problem Statement

Traditional wildfire response systems are reactive rather than predictive, leading to delayed containment and higher destruction rates.

Methodology

We implemented a Temporal Transformer Network combined with geospatial convolution layers to model fire spread probability over time and terrain variations.

Technology Stack

Results & Impact

Improved wildfire spread prediction accuracy by 38% compared to traditional statistical models and reduced response time by 30%.

Future Work

Integration with drone thermal feeds and real-time sensor networks for adaptive fire spread modeling.