Exploring and Analyzing Wildland Fire Data Via Machine Learning
  Techniques
Dipak Dulal Joseph J. Charney Michael Gallagher Carmeliza Navasca Nicholas Skowronski
This research project investigated the correlation between a 10 Hz time
series of thermocouple temperatures and turbulent kinetic energy (TKE) computed
from wind speeds collected from a small experimental prescribed burn at the
Silas Little Experimental Forest in New Jersey, USA. The primary objective of
this project was to explore the potential for using thermocouple temperatures
as predictors for estimating the TKE produced by a wildland fire. Machine
learning models, including Deep Neural Networks, Random Forest Regressor,
Gradient Boosting, and Gaussian Process Regressor, are employed to assess the
potential for thermocouple temperature perturbations to predict TKE values.
Data visualization and correlation analyses reveal patterns and relationships
between thermocouple temperatures and TKE, providing insight into the
underlying dynamics. The project achieves high accuracy in predicting TKE by
employing various machine learning models despite a weak correlation between
the predictors and the target variable. The results demonstrate significant
success, particularly from regression models, in accurately estimating the TKE.
The research findings contribute to fire behavior and smoke modeling science,
emphasizing the importance of incorporating machine learning approaches and
identifying complex relationships between fine-scale fire behavior and
turbulence. Accurate TKE estimation using thermocouple temperatures allows for
the refinement of models that can inform decision-making in fire management
strategies, facilitate effective risk mitigation, and optimize fire management
efforts. This project highlights the valuable role of machine learning
techniques in analyzing wildland fire data, showcasing their potential to
advance fire research and management practices.