Data assimilation using sequential monte carlo methods in wildfire spread simulation

Abstract

Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.

Random Finite Set Based Data Assimilation for Dynamic Data Driven Simulation of Maritime Pirate Activity

“…Experimental Design. We take advantage of the identicaltwin experiment introduced in [31,32] to verify the proposed data assimilation algorithm in ideal situations. In the identical-twin experiment, the simulation whose results are considered as real states is first executed with corresponding measurements which are collected during the process.…”

“…PF based data assimilation in traffic simulation is presented by Xie et al in [29] and Wu et al in [30]. PF based data assimilation in wildfire simulation is presented by Xue et al in [31,32]. They all use the same nonparametric statistic inference method based on particle filter, because the particle filter based data assimilation algorithms need no assumptions about the distribution or linearity of the studied simulation systems.…”

Activity-informed Dynamic Data Driven Simulation

“…The data assimilation uses real time sensor data and the simulation model to infer the state of the system and/or to tune the model parameters in real time. We developed data assimilation based on the Sequential Monte Carlo Methods in previous work (see [1,2] for more details). On top of data assimilation is the activity identification and behavior pattern recognition layer.…”

“…With recent advances in sensor and network technologies, the availability and fidelity of such real time data have greatly increased. To utilize real time sensor data for improving simulation results, we carried out research on dynamic data driven simulation (DDDS), where a simulation system is continually influenced by the real time data streams for better analysis and prediction of a system under study [1,2]. Figure 1 illustrates the idea of dynamic data driven simulation based on the application of wildfire spread simulation.…”

Traffic Incident Management in the Presence of Hazards

“…New innovative technology and systems should be developed to deliver both real-time and predicted information. This requires a multidiscipline approach, which combines the results from a wide of range of fields, e.g., fire simulation Hu, 2011;Xue et al, 2012), plume modeling (Zelle et al, 2013;García-Díaz and Gozalvez-Zafrilla, 2012), and traffic predictions (Li and Chen, 2014;Fei et al, 2013).…”