A comparative study of parameter estimation and state estimation approaches in data-driven wildfire spread modeling

Abstract

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Time aware genetic algorithm for forest fire propagation prediction: exploiting multi‐core platforms

“…There are different approaches in the literature oriented to overcoming each one of these problems; however, none is focused on accomplishing both requirements simultaneously. On the one hand, regarding the uncertainty in the input parameters, one can find approaches such as , where the main objective is to adjust a subset of the input parameters, taking into account the observed past evolution of the fire. In , the authors propose a steering strategy, which includes a data assimilation method based on the ensemble Kalman filter to adjust the rate of spread of the propagation model.…”

“…On the one hand, regarding the uncertainty in the input parameters, one can find approaches such as , where the main objective is to adjust a subset of the input parameters, taking into account the observed past evolution of the fire. In , the authors propose a steering strategy, which includes a data assimilation method based on the ensemble Kalman filter to adjust the rate of spread of the propagation model. The work aims to work with wildfire propagation fronts as an adjustment function after analyzing the problems of adjusting the linear rate of spread of the underlying model.…”

Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling

“…The requirement of parameter/hyperparameter tuning for different fire events (often "manually" as described in [33]) is not only computationally demanding but also limits generalisability. Much effort [33][34][35][36][37] has been made to enhance and generalise the parameter identification in wildfire modelling, which can be viewed as an inverse problem of fire forecasting. The work of [35] introduced a new uncertainty quantification approach for more efficient parameter calibration based on polynomial chaos expansion.…”

A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire

“…While the results were promising they also raised concerns on spurious fire corrections and the needed computing time. Following those ideas, Rochoux et al (2014aRochoux et al ( ,b, 2015 implemented a data-driven system based on a level-set description of Rothermel (1972) model and studied the difference between parameters and state assimilations. The system was further used in an interesting application to reproduce the front location of a three-hectare field-scale tests (Zhang et al, 2018) and a simulation recreating of a medium-scale (800 x 400 m) wildfire test (Zhang et al, 2017).…”