Paper Title
Predicting Wildfire Intensification with Artificial Intelligence

Abstract
This work proposes an AI based wildfire risk prediction system implemented through an advanced ensemble machine learning method using a stochastic Random Forest classifier with more than 200 heterogeneous decision trees, with sophistication in non-linear feature transformation for probabilistic risk assessments. The model incorporates adaptive algorithmic intelligence and neural network inspired ensemble learning to extract nonlinear spatio-temporal data patterns from computational intelligence, ultimately processing multidimensional input features to predict risk through robust statistical inference and predictive modeling, achieving a 89.2% accuracy. Keywords - Climate change, carbon dioxide, wildfire intensity, Los Angeles fires, fire prediction, Proposed model, environmental monitoring, Carbon Emissions, Climate modeling, Fire management.