Explainable AI Integrated Feature Engineering for Wildfire Prediction

TL;DR

This paper evaluates various machine learning models for wildfire prediction, emphasizing the importance of interpretability techniques like XAI to understand feature contributions and improve model transparency in environmental applications.

Contribution

The study introduces a hybrid neural network integrating numerical and image data, and applies multiple XAI methods to interpret wildfire prediction models, advancing transparency in environmental ML applications.

Findings

XGBoost outperforms in wildfire classification accuracy

Random Forest excels in predicting wildfire-affected areas

XAI techniques reveal key features influencing predictions

Abstract

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.