Machine learning based surrogate modelling and parameter identification for wildfire forecasting

Simulating wildfire propagation in near real-time is difficult due to the high computational cost and inappropriate choices of physics parameters used in the forecasting models. In this work, we first proposed a data-model integration scheme for fire progression forecasting, that combines deep learning models: reduced-order modelling, recurrent neural network (Long-Short-Term Memory) and data assimilation techniques. Capable of integrating real-time satellite observations, the deep learning-based surrogate model run about 1000 times faster than the Cellular Automata model used to forecast wildfires in real world scenarios. We then addressed the bottleneck of efficient physics parameter estimation by developing a novel inverse approach, relying on data assimilation in a reduced order space. Both the fire prediction and the parameter estimation approaches are tested over recent massive wildfire events in California with satellite observation from MODIS and VIIRS to adjust the fire forecasting in near real-time.