Projecting live fuel moisture content via deep learning

Lynn Miller, Liujun Zhu, Marta Yebra, Christoph Rüdiger, Geoffrey I. Webb

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)709-727
Number of pages19
JournalInternational Journal of Wildland Fire
Volume32
Issue number5
DOIs
Publication statusPublished - 2023

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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