A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk

2025/06/29 by Amina El Bernoussi, Bernoussi, Amina El, Mohamed El Arrouchi +1
Environmental Science · #FOS: Computer and information sciences #Fire effects on ecosystems #Hydrology and Drought Analysis #Methodology (stat.ME) #Wind and Air Flow Studies

paper · pdf · doi:10.48550/arxiv.2506.23161

openalex publication_date 2025/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This simulation study compares statistical approaches for estimating extreme quantile regression, with a specific application to fire risk forecasting. A simulation-based framework is designed to evaluate the effectiveness of different methods in capturing extreme dependence structures and accurately predicting extreme quantiles. These approaches are applied to fire occurrence data from the Fez-Meknes region, where a positive relationship is observed between increasing maximum temperatures and fire frequency. The study highlights the comparative performance of each technique and advocates for a hybrid strategy that combines their complementary strengths to enhance both the accuracy and interpretability of forecasts for extreme events.

Citations

• A New Regression Model for Analyzing Non-Stationary Extremes in Response and Covariate Variables with an Application in Meteorology
• Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
• Extremal Random Forests
• Gradient boosting with extreme-value theory for wildfire prediction
• Gradient boosting for extreme quantile regression
• Improving precipitation forecasts using extreme quantile regression
• Recent Developments in Bootstrap Methodology
• Regression Quantiles

Related

• Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
• Wildfire risk forecast: An optimizable fire danger index
• Extreme quantile regression with deep learning
• Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices
• Estimation and uncertainty quantification for extreme quantile regions