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International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE (Open Access)

Projecting live fuel moisture content via deep learning

Lynn Miller https://orcid.org/0000-0001-9899-9059 A * , Liujun Zhu B , Marta Yebra C D , Christoph Rüdiger E F and Geoffrey I. Webb A G
+ Author Affiliations
- Author Affiliations

A Department of Data Science and Artificial Intelligence, Monash University, Clayton, Vic. 3800, Australia.

B Yangtze Institute for Conservation and Development, Hohai University, Nanjing, 210024, China.

C Fenner School of Environment & Society, Australian National University, ACT 2601, Australia.

D School of Engineering, Australian National University, ACT 2601, Australia.

E Department of Civil Engineering, Monash University, Clayton, Vic. 3800, Australia.

F Science and Innovation Group, Bureau of Meteorology, Melbourne, Vic. 3008, Australia.

G Monash Data Futures Institute, Monash University, Clayton, Vic. 3800, Australia.

* Correspondence to: lynn.miller1@monash.edu

International Journal of Wildland Fire 32(5) 709-727 https://doi.org/10.1071/WF22188
Submitted: 23 August 2022  Accepted: 23 February 2023   Published: 20 March 2023

© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background: Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for high wildfire risk conditions. Many statistical models have been proposed to predict LFMC from remotely sensed data; however, almost all these estimate current LFMC (nowcasting models). Accurate modelling of LFMC in advance (projection models) would provide wildfire managers with more timely information for assessing and preparing for wildfire risk.

Aims: The aim of this study was to investigate the potential for deep learning models to predict LFMC across the continental United States 3 months in advance.

Method: Temporal convolutional networks were trained and evaluated using a large database of field measured samples, as well as year-long time series of MODerate resolution Imaging Spectroradiometer (MODIS) reflectance data and Parameter-elevation Relationships on Independent Slopes Model (PRISM) meteorological data.

Key results: The proposed 3-month projection model achieved an accuracy (root mean squared error (RMSE) 27.52%; R2 0.47) close to that of the nowcasting model (RMSE 26.52%; R2 0.51).

Conclusions: The study is the first to predict LFMC with a 3-month lead-time, demonstrating the potential for deep learning models to make reliable LFMC projections.

Implications: These findings are beneficial for wildfire management and risk assessment, showing proof-of-concept for providing advance information useful to help mitigate the effect of catastrophic wildfires.

Keywords: convolutional neural network, deep learning ensembles, fire danger, live fuel moisture content, meteorological data, MODIS, remote sensing, time series analysis.


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