Social & Physics Based Data Driven Methods for Wildfire Prediction

Abstract

<p>Twitter is increasingly being used as a real-time human-sensor network during natural disasters, detecting, tracking and documenting events. Current wildfire models currently largely omit social media data, representing a shortcoming in current models, as valuable and timely information is transmitted via this channel. By including this data as a real-time data source, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This monitoring model is coupled to a real-time forecasting of wildfire dynamics.</p><p>Real-time forecasting of wildfire dynamics, which has attracted increasing attention recently in fire safety science, is extremely challenging due to the complexities of the physical models and the geographical features. Running physics-based simulations for large-scale wildfires can be computationally difficult. We propose a novel algorithm scheme, which combines reduced-order modelling (ROM), recurrent neural networks (RNN), data assimilation (DA) and error covariance tuning for real-time forecasting/monitoring of the burned area. An operating cellular automata (CA) simulator is used to compute a data-driven surrogate model for forecasting fire diffusions. A long-short-term-memory (LSTM) neural network is used to build sequence-to-sequence predictions following the simulation results projected/encoded in a reduced-order latent space. </p><p>We implement machine learning in a wildfire prediction model, using social media and geophysical data sources with sentiment analysis to predict wildfire instances and characteristics with high accuracy. The geophysical data is satellite data provided by the Global Fire Atlas, and social data is provided by Twitter. In doing this, we perform our own data collection and analysis, comparing regional differences in online social sentiment expression.</p><p>The performance of the proposed algorithm has been tested in recent massive wildfire events in California.</p>