Di Fan

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.

Di Fan, Chuanhou Gao

The (variational) graph auto-encoder is widely used to learn representations for graph-structured data. However, the formation of real-world graphs is a complicated and heterogeneous process influenced by latent factors. Existing encoders are fundamentally holistic, neglecting the entanglement of latent factors. This reduces the effectiveness of graph analysis tasks, while also making it more difficult to explain the learned representations. As a result, learning disentangled graph representations with the (variational) graph auto-encoder poses significant challenges and remains largely unexplored in the current research. In this paper, we introduce the Disentangled Graph Auto-Encoder (DGA) and the Disentangled Variational Graph Auto-Encoder (DVGA) to learn disentangled representations. Specifically, we first design a disentangled graph convolutional network with multi-channel message-passing layers to serve as the encoder. This allows each channel to aggregate information about each latent factor. The disentangled variational graph auto-encoder's expressive capability is then enhanced by applying a component-wise flow to each channel. In addition, we construct a factor-wise decoder that takes into account the characteristics of disentangled representations. We improve the independence of representations by imposing independence constraints on the mapping channels for distinct latent factors. Empirical experiments on both synthetic and real-world datasets demonstrate the superiority of our proposed method compared to several state-of-the-art baselines.