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Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire). • Data-driven calibration to deal with uncertainty in forest fire spread prediction. • Cloud-based urgent computing implementation of a two-stage prediction model. • Use of utility function to deal with the cost-performance trade-off. • Validation against a deadly and destructive wildfire with promising results. [ABSTRACT FROM AUTHOR]

The wildland-urban interface (WUI) is defined as a geographic area where human developments and flammable vegetation merge in a wildfire-prone environment. Losses due to wildfire have been rising in the past decade, attributed to changes in vegetation growth, fuel availability, and increased land developments in WUI. This paper studies the process of wildfire spread inside WUI communities. The fire spread rate within WUI communities is determined for nine wildfires that were ranked among the most destructive wildfires in North America. An improved quasi-empirical model that considers radiation and fire spotting as modes of fire spread inside a community is proposed. The new model is validated using the documented spread rates during the 2007 Witch and Guejito fires and the 2017 Tubbs fire. The proposed model is computationally efficient and can be used to quantify fire spread rate and the number of affected structures inside a community during a wildfire event. • Communities are being exposed to faster spreading wildfires in recent years. • A streamlined integrative urban fire spread model is proposed. • The proposed model is applied to two case studies in California. • The proposed model is capable of capturing fire progression in WUI communities when compared to real scenarios. [ABSTRACT FROM AUTHOR]