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Natural hazard risk management is a demanding interdisciplinary task. It requires domain knowledge, integration of robust computational methods, and effective use of complex datasets. However, existing solutions tend to focus on specific aspects, data, or methods, limiting their impact and applicability. Here, we present a general data-driven framework to support risk assessment and policy making illustrating its usage in the context of fire hazard by integrating three unique datasets of fire behavior, street network, and census data for the whole state of California. We show that integrating spatial complexity by including a fire behavior layer and a socio-demographic layer changes the universal function observed in previous optimization frameworks that only work with the accessibility of facilities. These results open avenues for the future development of flexible interdisciplinary frameworks in natural hazards management using complex large-scale data., Comment: 17 pages, 6 figures

In practical applications, it is common that wildfire simulators do not correctly predict the evolution of the fire scar. Usually, this is caused due to multiple factors including inaccuracy in the input data such as land cover classification, moisture, improperly represented local winds, cumulative errors in the fire growth simulation model, high level of discontinuity/heterogeneity within the landscape, among many others. Therefore in practice, it is necessary to adjust the propagation of the fire to obtain better results, either to support suppression activities or to improve the performance of the simulator considering new default parameters for future events, best representing the current fire spread growth phenomenon. In this article, we address this problem through a new methodology using Derivative-Free Optimization (DFO) algorithms for adjusting the Rate of Spread (ROS) factors in a fire simulation growth model called Cell2Fire. To achieve this, we solve an error minimization optimization problem that captures the difference between the simulated and observed fire, which involves the evaluation of the simulator output in each iteration as part of a DFO framework, allowing us to find the best possible factors for each fuel present on the landscape. Numerical results for different objective functions are shown and discussed, including a performance comparison of alternative DFO algorithms., Comment: 8 figures, 35 pages