Explainable AI Integrated Feature Engineering for Wildfire Prediction
Di Fan Ayan Biswas James Paul Ahrens
Wildfires present intricate challenges for prediction, necessitating the use
of sophisticated machine learning techniques for effective
modeling\cite{jain2020review}. In our research, we conducted a thorough
assessment of various machine learning algorithms for both classification and
regression tasks relevant to predicting wildfires. We found that for
classifying different types or stages of wildfires, the XGBoost model
outperformed others in terms of accuracy and robustness. Meanwhile, the Random
Forest regression model showed superior results in predicting the extent of
wildfire-affected areas, excelling in both prediction error and explained
variance. Additionally, we developed a hybrid neural network model that
integrates numerical data and image information for simultaneous classification
and regression. To gain deeper insights into the decision-making processes of
these models and identify key contributing features, we utilized eXplainable
Artificial Intelligence (XAI) techniques, including TreeSHAP, LIME, Partial
Dependence Plots (PDP), and Gradient-weighted Class Activation Mapping
(Grad-CAM). These interpretability tools shed light on the significance and
interplay of various features, highlighting the complex factors influencing
wildfire predictions. Our study not only demonstrates the effectiveness of
specific machine learning models in wildfire-related tasks but also underscores
the critical role of model transparency and interpretability in environmental
science applications.