Learning Wildfire Model from Incomplete State Observations
- URL: http://arxiv.org/abs/2111.14038v1
- Date: Sun, 28 Nov 2021 03:21:46 GMT
- Title: Learning Wildfire Model from Incomplete State Observations
- Authors: Alissa Chavalithumrong, Hyung-Jin Yoon, Petros Voulgaris
- Abstract summary: We create a dynamic model for future wildfire predictions of five locations within the western United States through a deep neural network.
The proposed model has distinct features that address the characteristic need in prediction evaluations, including dynamic online estimation and time-series modeling.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: As wildfires are expected to become more frequent and severe, improved
prediction models are vital to mitigating risk and allocating resources. With
remote sensing data, valuable spatiotemporal statistical models can be created
and used for resource management practices. In this paper, we create a dynamic
model for future wildfire predictions of five locations within the western
United States through a deep neural network via historical burned area and
climate data. The proposed model has distinct features that address the
characteristic need in prediction evaluations, including dynamic online
estimation and time-series modeling. Between locations, local fire event
triggers are not isolated, and there are confounding factors when local data is
analyzed due to incomplete state observations. When compared to existing
approaches that do not account for incomplete state observation within wildfire
time-series data, on average, we are able to achieve higher prediction
performances.
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