Open Access
June 2011 Point process modeling of wildfire hazard in Los Angeles County, California
Haiyong Xu, Frederic Paik Schoenberg
Ann. Appl. Stat. 5(2A): 684-704 (June 2011). DOI: 10.1214/10-AOAS401


The Burning Index (BI) produced daily by the United States government’s National Fire Danger Rating System is commonly used in forecasting the hazard of wildfire activity in the United States. However, recent evaluations have shown the BI to be less effective at predicting wildfires in Los Angeles County, compared to simple point process models incorporating similar meteorological information. Here, we explore the forecasting power of a suite of more complex point process models that use seasonal wildfire trends, daily and lagged weather variables, and historical spatial burn patterns as covariates, and that interpolate the records from different weather stations. Results are compared with models using only the BI. The performance of each model is compared by Akaike Information Criterion (AIC), as well as by the power in predicting wildfires in the historical data set and residual analysis. We find that multiplicative models that directly use weather variables offer substantial improvement in fit compared to models using only the BI, and, in particular, models where a distinct spatial bandwidth parameter is estimated for each weather station appear to offer substantially improved fit.


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Haiyong Xu. Frederic Paik Schoenberg. "Point process modeling of wildfire hazard in Los Angeles County, California." Ann. Appl. Stat. 5 (2A) 684 - 704, June 2011.


Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62168
MathSciNet: MR2840171
Digital Object Identifier: 10.1214/10-AOAS401

Keywords: Burning index , Conditional intensity , point process , residual analysis

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.5 • No. 2A • June 2011
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