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FIRO: A Deep-neural Network for Wildfire Forecast with Interpretable Hidden States
Eduardo Rodrigues · Campbell Watson · Bianca Zadrozny · Gabrielle Nyirjesy
Event URL: https://www.climatechange.ai/papers/neurips2022/31 »

Several wildfire danger systems have emerged from decades of research. One such system is the National Fire-Danger Rating System (NFDRS), which is used widely across the United States and is a key predictor in the Global ECMWF Fire Forecasting (GEFF) model. The NFDRS is composed of over 100 equations relating wildfire risk to weather conditions, climate and land cover characteristics, and fuel. These equations and the corresponding 130+ parameters were developed via field and lab experiments. These parameters, which are fixed in the standard NFDRS and GEFF implementations, may not be the most appropriate for a climate-changing world. In order to adjust the NFDRS parameters to current climate conditions and specific geographical locations, we recast NFDRS in PyTorch to create a new deep learning-based Fire Index Risk Optimizer (FIRO). FIRO predicts the ignition component, or the probability a wildfire would require suppression in the presence of a firebrand, and calibrates the uncertain parameters for a specific region and climate conditions by training on observed fires. Given the rare occurrence of wildfires, we employed the extremal dependency index (EDI) as the loss function. Using ERA5 reanalysis and MODIS burned area data, we trained FIRO models for California, Texas, Italy, and Madagascar. Across these four geographies, the average EDI improvement was 175% above the standard NFDRS implementation

Author Information

Eduardo Rodrigues (Microsoft Research)
Campbell Watson (IBM Research)

I'm an atmospheric scientist at IBM Research where my research spans climate, weather and water. I was a postdoc at Yale University with Prof. Ron Smith, and completed a PhD at the University of Melbourne with Prof. Todd Lane. Currently leading AI for Climate initiatives with the Future of Climate at IBM Research.

Bianca Zadrozny (IBM Research)
Gabrielle Nyirjesy (Columbia University)

Gabby Nyirjesy is a Master’s in Data Science student at Columbia University. Her prior education includes a B.S. in Materials Science and Engineering from Johns Hopkins University in 2016. Gabby has 5 years of experience implementing machine learning models for her clients at Accenture.

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