Identification

Title

Enhancing wildfire spread modelling by building a gridded fuel moisture content product with machine learning

Abstract

Wildland fire decision support systems require accurate predictions of wildland fire spread. Fuel moisture content (FMC) is one of the important parameters controlling the rate of spread of wildland fire. However, dead FMC measurements are provided by a relatively sparse network of remote automatic weather stations (RAWS), while live FMC is relatively infrequently measured manually. We developed a high resolution, gridded, real-time FMC data sets that did not previously exist for assimilation into operational wildland fire prediction systems based on ML. We used surface observations of live and dead FMC to train machine learning models to estimate FMC based on satellite observations. Moderate Resolution Imaging Spectrometer Terra and Aqua reflectances are used to predict the live and dead FMC measured by the Wildland Fire Assessment System and RAWS). We evaluate multiple machine learning methods including multiple linear regression, random forests (RFs), gradient boosted regression and artificial neural networks. The models are trained to learn the relationships between the satellite reflectances, surface weather and soil moisture observations and FMC. After training on data corresponding to the temporally and spatially nearest grid points to the irregularly spaced surface FMC observations, the machine learning models could be applied to all grid cells for a gridded product over the Conterminous United States (CONUS). The results show generally that the rule-based approaches have the lowest errors likely due to the sharp decision boundaries among the predictors, and the RF approach that utilizes bagging to avoid over-fitting has the lowest error on the test dataset. The errors are typically between 25%âˆ’33% the typical variability of the FMC data, which indicate the skill of the RF in estimating the FMC based on satellite data and surface characteristics. The FMC gridded product based on the RF runs operationally daily over CONUS and can be assimilated into WRF-Fire for more accurate wildland fire spread predictions.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7th8r4p

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2020-08-20T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact