Wildfires play a critical role in determining ecosystem structure and function and pose serious risks to human life, property and ecosystem services. Burn probability (BP) models the likelihood that a location could burn. Simulation models are typically used to predict BP but are computationally intensive. Machine learning (ML) pipelines can predict BP and reduce computational intensity. In this work, we tested approaches to reduce the set of input features used in an ML model to estimate BP for the state of California, USA, without loss of predictive performance. We used Principal Component Analysis (PCA) to determine the optimal set of features to use in our ML pipeline. Then, we mapped BP and compared model performance when using the reduced set and when using the whole set of features. Models using optimized input achieved similar prediction performance while using less than 50% of the input features.
|Title||Determination of optimal set of spatio-temporal features for predicting burn probability in the state of California, USA|
|Authors||Javier Andres Pastorino Gonzalez, Joseph Willliams Director, Ashis K Biswas, Todd Hawbaker|
|Publication Type||Conference Paper|
|Publication Subtype||Conference Paper|
|Record Source||USGS Publications Warehouse|
|USGS Organization||Geosciences and Environmental Change Science Center|