The lack of accurate prediction of wildfire susceptibility under warming scenarios

The gap

Evidence profile

Sourced from the stated research gap and abstract of the source papers, classified as general, drawn from work published between 2021 and 2026, spanning 4 journals. Those papers have been cited 157 times in total.

Supporting evidence — 5 representative gaps

• Bridging Climate and Socio-Environmental Vulnerability for Wildfire Risk Assessment Using Explainable Machine Learning: Evidence from the 2025 Wildfire in Korea (2026) · Forests · cited 3× · doi

  There is a need to integrate environmental drivers, explainable machine-learning modeling, and socio-environmental vulnerability assessment to predict and interpret wildfire susceptibility. The study identifies a gap in understanding the spatial relationship between ignition-prone environments and socio-environmental features.

  generalstated research gap
  Keywords: there need integrate environmental drivers explainable machine-learning modeling
• XGBoost-Based Susceptibility Model Exhibits High Accuracy and Robustness in Plateau Forest Fire Prediction (2026) · Forests · cited 5× · doi

  The paper identifies the uncertainty in forest fire susceptibility prediction under future climate change as a research gap. The study highlights the need for a robust model that can capture nonlinear relationships between variables. The paper notes that prior studies have not effectively addressed the issue of forest fire susceptibility prediction in climate-sensitive plateau regions.

  generalstated research gap
  Keywords: paper identifies uncertainty forest fire susceptibility prediction future
• Influence of global climate modes on wildfire occurrence in the contiguous United States under recent and future climates (2025) · Climate Dynamics · cited 1× · doi

  Although these global climate modes have been associated with historical variability in wildfire occurrence in the United States and are used to forecast seasonal wildfire risk, precise information about the spatial pattern and magnitude of their influence is lacking and the satellite record of wildfires is too short to address these issues.

  generalabstract
  Keywords: wildfire global climate modes associated historical variability occurrence united states used forecast seasonal risk precise
• Reviews and syntheses: Arctic fire regimes and emissions in the 21st century (2021) · Biogeosciences · cited 148× · doi

  (6) Factors contributing to the uncertainty of predicting and quantifying future Arctic fire regimes include underestimation of Arctic fires by satellite systems, lack of agreement between Earth observations and official statistics, and still needed refinements of location, conditions, and previous fire return intervals on peat and permafrost landscapes.

  generalabstractevidence 5/5
  Keywords: arctic fire factors contributing uncertainty predicting quantifying future regimes include underestimation fires satellite systems lack
• GIS-Integrated Machine Learning for Wildfire Susceptibility Mapping Under Temperature Increase Scenarios: A Bayesian-Optimized ANN Approach (2026) · ISPRS International Journal of Geo-Information · doi

  The lack of accurate prediction of wildfire susceptibility under warming scenarios. The need for a spatially explicit framework for proactive mitigation and climate-resilient land-use planning. The limitation of existing wildfire susceptibility mapping approaches in incorporating climate scenarios.

  generalstated research gapevidence 5/5
  Keywords: lack accurate prediction wildfire susceptibility warming scenarios need

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