In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Cheng:2022:10.3390/rs14133228,
author = {Cheng, S and Jin, Y and Harrison, S and Quilodrán, Casas C and Prentice, C and Guo, Y-K and Arcucci, R},
doi = {10.3390/rs14133228},
journal = {Remote Sensing},
title = {Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling},
url = {http://dx.doi.org/10.3390/rs14133228},
volume = {14},
year = {2022}
}
TY - JOUR
AB - Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.
AU - Cheng,S
AU - Jin,Y
AU - Harrison,S
AU - Quilodrán,Casas C
AU - Prentice,C
AU - Guo,Y-K
AU - Arcucci,R
DO - 10.3390/rs14133228
PY - 2022///
SN - 2072-4292
TI - Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling
T2 - Remote Sensing
UR - http://dx.doi.org/10.3390/rs14133228
UR - https://www.mdpi.com/2072-4292/14/13/3228
UR - http://hdl.handle.net/10044/1/97961
VL - 14
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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