ADTT TRANSFORMER FOR REAL-TIME FOREST FIRE RISK PREDICTION AND ECOSYSTEM MONITORING

Abstract

Forest fires represent a major threat to ecosystems, biodiversity, and human safety, particularly under accelerating climate change. Global wildfires destroyed 3.7 million km2 of land in 2024, with Brazil, Bolivia and Venezula most affected and recent accident of forest fire in USA in 2025 highlighting sudden climate changes escalating impacts. Accurate and timely prediction of forest fire risk is challenging due to the complex interactions among meteorological conditions, vegetation dynamics, and long-term temporal dependencies. This paper proposes a novel approach Advance Dynamic Digital Twin–Driven Transformer (ADTT) framework for real-time forest fire risk prediction and ecosystem monitoring. The framework integrates multi-source environmental data, including meteorological observations, satellite-derived vegetation indices, and historical fire records, into a dynamic digital twin of forest ecosystems. A climate-aware transformer-based time series forecasting model is embedded within the digital twin to capture long-range dependencies in multivariate environmental data. Additionally, a physics-guided loss function and multi-task learning strategy are introduced to enhance prediction accuracy and robustness. Experimental evaluation on real-world datasets demonstrates that the proposed hybrid approach outperforms traditional machine learning and recurrent neural network baselines in forecasting ecosystem states and predicting fire risk. The results highlight the potential of digital twin enabled transformers for proactive forest fire management and climate resilience by achieving 98% accuracy and fast response of the model.