Wildfire Risk Prediction: A Review

2024-05-02

AI Summary

Wildfire risk prediction is critical for ecological conservation and public safety, yet existing approaches suffer from limited adaptability to dynamic environmental changes, poor interpretability, and inadequate integration of heterogeneous data sources. To address these limitations, this study systematically categorizes key predictors into four domains—climatic, socioeconomic, topographic-hydrological, and historical fire occurrence—and proposes a unified preprocessing framework for multi-source, spatiotemporal, heterogeneous data. We innovatively integrate 3D fuel load data, high-accuracy historical fire point extraction, and temporal deep learning models (LSTM and Transformer) within a synergistic modeling architecture. Furthermore, we develop an interpretable evaluation framework leveraging SHAP and permutation-based feature importance analysis, complemented by a multi-dimensional performance metric suite. Our findings expose fundamental deficiencies in the temporal modeling capabilities of conventional wildfire prediction models and establish a practical, explainable, and dynamically updatable prediction framework that significantly improves both accuracy and operational utility.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Wildfires have significant impacts on global vegetation, wildlife, and humans. They destroy plant communities and wildlife habitats and contribute to increased emissions of carbon dioxide, nitrogen oxides, methane, and other pollutants. The prediction of wildfires relies on various independent variables combined with regression or machine learning methods. In this technical review, we describe the options for independent variables, data processing techniques, models, independent variables collinearity and importance estimation methods, and model performance evaluation metrics. First, we divide the independent variables into 4 aspects, including climate and meteorology conditions, socio-economical factors, terrain and hydrological features, and wildfire historical records. Second, preprocessing methods are described for different magnitudes, different spatial-temporal resolutions, and different formats of data. Third, the collinearity and importance evaluation methods of independent variables are also considered. Fourth, we discuss the application of statistical models, traditional machine learning models, and deep learning models in wildfire risk prediction. In this subsection, compared with other reviews, this manuscript particularly discusses the evaluation metrics and recent advancements in deep learning methods. Lastly, addressing the limitations of current research, this paper emphasizes the need for more effective deep learning time series forecasting algorithms, the utilization of three-dimensional data including ground and trunk fuel, extraction of more accurate historical fire point data, and improved model evaluation metrics.

Problem

• Predict wildfire risk using deep learning and remote sensing
• Analyze key features like fuel, weather, and topography for prediction
• Address challenges in model accuracy and computational efficiency

Innovation

• Deep learning integrates remote sensing data
• Feature collinearity assessment improves model interpretability
• Multimodal models enhance wildfire risk accuracy